From b837338c1e07a983b1b0b137e6507a1e522a0357 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 11:07:02 +0100
Subject: [PATCH 001/125] feat: Added initial Pytorch example to monitor
 batching and per layer metrics

---
 .../pytorch_image_classification.ipynb        | 445 ++++++++++++++++++
 1 file changed, 445 insertions(+)
 create mode 100644 integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
new file mode 100644
index 0000000..9ca474c
--- /dev/null
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neptune + PyTorch\n",
+    "\n",
+    "Introduction\n",
+    "\n",
+    "This guide will show you how to:\n",
+    "- Initialize the Neptune Run object\n",
+    "- Log standard loss and accuracy metrics to Neptune\n",
+    "- Log per layer activations and gradients for debugging model training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Before you start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install Neptune and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install dependencies\n",
+    "! pip install -q -U neptune_scale torch torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO - update config to include model architecture\n",
+    "# TODO - Add more hyperparameters\n",
+    "# TODO - Add additional logging metrics (weights, gradients, activations, etc.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Hyperparameters for Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    \"optimizer\": \"Adam\",\n",
+    "    \"batch_size\": 512,\n",
+    "    \"learning_rate\": 0.05,\n",
+    "    \"epochs\": 5, \n",
+    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+    "    \"input_features\": 256,\n",
+    "    \"n_classes\": 10,\n",
+    "    \"input_size\": 28 * 28\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Transform to normalize the data and convert it to tensor\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize((0.5,), (0.5,))  # Normalizing the image to range [-1, 1]\n",
+    "])\n",
+    "\n",
+    "# Download and load the MNIST dataset\n",
+    "train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
+    "val_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)  # Use test set as validation\n",
+    "test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
+    "\n",
+    "# DataLoader for training, validation, and testing\n",
+    "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adam\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Simple Convolutional Neural Network model for MNIST\n",
+    "class SimpleCNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(SimpleCNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)  # Input channels = 1 (grayscale images)\n",
+    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
+    "        self.fc1 = nn.Linear(64 * 7 * 7, 128)  # Flattened size of image after convolution layers\n",
+    "        self.fc2 = nn.Linear(128, 10)  # 10 output classes for digits 0-9\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv1(x))\n",
+    "        x = F.max_pool2d(x, 2)  # Pooling layer to downsample\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = F.max_pool2d(x, 2)\n",
+    "        x = x.view(-1, 64 * 7 * 7)  # Flatten the tensor for the fully connected layer\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "    \n",
+    "class SimpleNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(SimpleNN, self).__init__()\n",
+    "        # Define layers (increase number of layers)\n",
+    "        self.fc1 = nn.Linear(params[\"input_size\"], params[\"input_features\"]) \n",
+    "        self.fc2 = nn.Linear(params[\"input_features\"], 64)\n",
+    "        self.fc3 = nn.Linear(64, params[\"n_classes\"])      # Output layer (10 classes for MNIST)\n",
+    "\n",
+    "        # Registering hooks to track activations\n",
+    "        self.hooks = []\n",
+    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
+    "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
+    "        self.hooks.append(self.fc3.register_forward_hook(self.save_activation(\"fc3\")))\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x.view(-1, params[\"input_size\"])  # Flatten the input image (28x28)\n",
+    "        x = torch.relu(self.fc1(x))  # Apply ReLU activation\n",
+    "        x = torch.relu(self.fc2(x))  # Apply ReLU activation\n",
+    "        x = self.fc3(x)  # Output layer\n",
+    "        return x\n",
+    "    \n",
+    "        # Function to save activations\n",
+    "    def save_activation(self, name):\n",
+    "        def hook(model, input, output):\n",
+    "            self.activations[name] = output\n",
+    "        return hook\n",
+    "    \n",
+    "    def get_activations(self):\n",
+    "        return self.activations\n",
+    "\n",
+    "    def clear_activations(self):\n",
+    "        self.activations = {}\n",
+    "\n",
+    "# Instantiate the model, loss function, and optimizer\n",
+    "# model = SimpleCNN()\n",
+    "model = SimpleNN()\n",
+    "criterion = nn.CrossEntropyLoss()  # Loss function\n",
+    "\n",
+    "# Select an optimizer\n",
+    "if params[\"optimizer\"] == \"Adam\":\n",
+    "    optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
+    "    print(params[\"optimizer\"])\n",
+    "elif params[\"optimizer\"] == \"SGD\":\n",
+    "    optimizer = optim.SGD(model.parameters(), lr=params[\"learning_rate\"])\n",
+    "    print(params[\"optimizer\"])\n",
+    "else:\n",
+    "    print(\"No optimizer selected\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to evaluate the model (validation/test) with gradients tracked\n",
+    "def evaluate(model, data_loader, track_gradients=False):\n",
+    "    model.train() if track_gradients else model.eval()  # Ensure model is in training mode if tracking gradients\n",
+    "    correct_preds = 0\n",
+    "    total_preds = 0\n",
+    "    epoch_loss = 0\n",
+    "    with torch.no_grad():  # Disable gradient tracking during evaluation\n",
+    "        for data, target in data_loader:\n",
+    "            # Forward pass (with gradient tracking if specified)\n",
+    "            output = model(data)\n",
+    "            loss = criterion(output, target)  # Correct loss computation\n",
+    "            epoch_loss += loss.item()\n",
+    "            \n",
+    "            if track_gradients:\n",
+    "                # Track gradients (we will backpropagate but do not update model parameters)\n",
+    "                loss.backward()\n",
+    "            \n",
+    "            # Calculate accuracy\n",
+    "            _, predicted = torch.max(output.data, 1)\n",
+    "            total_preds += target.size(0)\n",
+    "            correct_preds += (predicted == target).sum().item()\n",
+    "    \n",
+    "    accuracy = 100 * correct_preds / total_preds\n",
+    "    return epoch_loss / len(data_loader), accuracy\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neptune - Initialize Training Run and Log Configs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: NEPTUNE_API_TOKEN=\"your_api_token\"\n",
+      "env: NEPTUNE_PROJECT=\"workspace_name/project_name\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set environment variable for the API token\n",
+    "%env NEPTUNE_API_TOKEN = \"your_api_token\"\n",
+    "%env NEPTUNE_PROJECT = \"workspace_name/project_name\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Neptune parameters\n",
+    "from neptune_scale import Run\n",
+    "from random import random\n",
+    "\n",
+    "custom_name = \"torch-vision\"\n",
+    "custom_id = random()\n",
+    "\n",
+    "run = Run(\n",
+    "    run_id=f\"{custom_name}-{custom_id}\"\n",
+    "    )\n",
+    "\n",
+    "run.log_configs(\n",
+    "    {\n",
+    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
+    "        \"config/optimizer\": params[\"optimizer\"],\n",
+    "        \"config/batch_size\": params[\"batch_size\"],\n",
+    "        \"config/epochs\": params[\"epochs\"]\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
+    "run.add_tags(tags=[\"Torch-MINST\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neptune - Log Metrics while Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Training loop\n",
+    "num_epochs = params[\"epochs\"]\n",
+    "step_counter = 0\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    epoch_loss = 0\n",
+    "    correct_preds = 0\n",
+    "    total_preds = 0\n",
+    "\n",
+    "    # Reset activations for each epoch\n",
+    "    model.clear_activations()\n",
+    "    \n",
+    "    # Training step\n",
+    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
+    "        step_counter += 1\n",
+    "        optimizer.zero_grad()\n",
+    "        \n",
+    "        # Forward pass\n",
+    "        output = model(data)\n",
+    "        \n",
+    "        # Compute the loss\n",
+    "        loss = criterion(output, target)\n",
+    "        epoch_loss += loss.item()\n",
+    "        \n",
+    "        # Backward pass and optimization\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        # Calculate accuracy\n",
+    "        _, predicted = torch.max(output.data, 1)\n",
+    "        total_preds += target.size(0)\n",
+    "        correct_preds += (predicted == target).sum().item()\n",
+    "        \n",
+    "        # Print loss and accuracy for each batch (step)\n",
+    "        #if (batch_idx + 1) % 5 == 0:  # Every 5 steps\n",
+    "        batch_accuracy = 100 * correct_preds / total_preds\n",
+    "        print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\n",
+    "                \n",
+    "        # Validation step per training step\n",
+    "        val_loss, val_accuracy = evaluate(model, val_loader)  # Evaluate after each step\n",
+    "        print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\n",
+    "\n",
+    "        run.log_metrics(\n",
+    "            data = {\n",
+    "                \"metrics/train/loss\": loss.item(),\n",
+    "                \"metrics/train/accuracy\": batch_accuracy,\n",
+    "                \"metrics/validation/loss\": val_loss,\n",
+    "                \"metrics/validation/accuracy\": val_accuracy,\n",
+    "                \"epoch_value\": epoch\n",
+    "            },\n",
+    "            step = step_counter\n",
+    "        )\n",
+    "    \n",
+    "    # Print loss and accuracy for the entire training epoch\n",
+    "    train_accuracy = 100 * correct_preds / total_preds\n",
+    "    print(f\"Epoch [{epoch+1}/{num_epochs}] Training complete. Loss: {epoch_loss / len(train_loader):.4f}, Accuracy: {train_accuracy:.2f}%\")\n",
+    "\n",
+    "    # Track activations and gradients per layer\n",
+    "    activation_dict_mean = {\n",
+    "        f\"layers/layer_{name}/activation_mean\": activation.mean().item() for name, activation in model.get_activations().items()\n",
+    "        }\n",
+    "    \n",
+    "    activation_dict_std = {\n",
+    "        f\"layers/layer_{name}/activation_std\": activation.std().item() for name, activation in model.get_activations().items()\n",
+    "    }\n",
+    "\n",
+    "    params_dict_std = {\n",
+    "            f\"layers/layer_{name.split(\".\")[0]}/{name.split(\".\")[1]}_std\": param.grad.std().item() for name, param in model.named_parameters()\n",
+    "        }\n",
+    "    \n",
+    "    params_dict_mean = {\n",
+    "            f\"layers/layer_{name.split(\".\")[0]}/{name.split(\".\")[1]}_mean\": param.grad.mean().item() for name, param in model.named_parameters()\n",
+    "        }\n",
+    "    \n",
+    "    layers_dict = {**activation_dict_mean, \n",
+    "                   **activation_dict_std,\n",
+    "                   **params_dict_mean,\n",
+    "                   **params_dict_std\n",
+    "                   }\n",
+    "    print(layers_dict)\n",
+    "\n",
+    "    # data_to_log = {\n",
+    "    #        \"metrics/test/loss_epoch\": epoch_loss / len(train_loader),\n",
+    "     #       \"metrics/train/accuracy_epoch\": train_accuracy\n",
+    "      #  }.update(activation_dict)\n",
+    "    \n",
+    "    run.log_metrics(\n",
+    "    data = layers_dict,\n",
+    "    step = epoch\n",
+    "    )\n",
+    "    \n",
+    "# Final Testing Step with gradient tracking\n",
+    "test_loss, test_accuracy = evaluate(model, test_loader, track_gradients=False)  # Track gradients during test\n",
+    "print(f\"Testing complete. Loss: {test_loss:.4f}, Accuracy: {test_accuracy:.2f}%\")\n",
+    "\n",
+    "run.log_configs(\n",
+    "        {\n",
+    "        \"metrics/test/loss\": test_loss,\n",
+    "        \"metrics/test/accuracy\": test_accuracy\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "neptune:INFO: Waiting for all operations to be processed\n",
+      "neptune:WARNING: No timeout specified. Waiting indefinitely\n",
+      "neptune:INFO: All operations were processed\n"
+     ]
+    }
+   ],
+   "source": [
+    "run.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neptune_scale_py_312_base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 9994528160aa6dd4c7032a1b35ee835b2305fdce Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 11:17:48 +0100
Subject: [PATCH 002/125] refactor: Update introduction section for more
 clarity on notebook use

---
 .../pytorch/pytorch_image_classification.ipynb              | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 9ca474c..2e823f0 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -9,9 +9,11 @@
     "Introduction\n",
     "\n",
     "This guide will show you how to:\n",
-    "- Initialize the Neptune Run object\n",
+    "- Initialize the Neptune Run object and log configuration parameters\n",
     "- Log standard loss and accuracy metrics to Neptune\n",
-    "- Log per layer activations and gradients for debugging model training"
+    "- Log debugging metrics during model training such as;\n",
+    "    * Activations per layer\n",
+    "    * Gradients (mean and std weights and biases) per layer"
    ]
   },
   {

From 223fde15fc8b3cc01c703477a65910555b265b2e Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 11:30:53 +0100
Subject: [PATCH 003/125] chore: change how the custom run id gets
 automatically generated

---
 .../pytorch/pytorch_image_classification.ipynb        | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 2e823f0..454c431 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -259,20 +259,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Define Neptune parameters\n",
     "from neptune_scale import Run\n",
-    "from random import random\n",
-    "\n",
-    "custom_name = \"torch-vision\"\n",
-    "custom_id = random()\n",
+    "from uuid import uuid4\n",
     "\n",
-    "run = Run(\n",
-    "    run_id=f\"{custom_name}-{custom_id}\"\n",
-    "    )\n",
+    "run = Run(run_id=f\"pytorch-{uuid4()}\")\n",
     "\n",
     "run.log_configs(\n",
     "    {\n",

From ceb3a249f904348c56891bab7452242fde797901 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 11:33:16 +0100
Subject: [PATCH 004/125] chore: update instructions on how users can get and
 set their API token and project name

---
 .../pytorch_image_classification.ipynb        | 26 ++++---------------
 1 file changed, 5 insertions(+), 21 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 454c431..13af69e 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -20,7 +20,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Before you start"
+    "## Before you start\n",
+    "\n",
+    "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
+    "  2. Create a Neptune project that you will use for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
+    "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
    ]
   },
   {
@@ -237,26 +241,6 @@
     "## Neptune - Initialize Training Run and Log Configs"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: NEPTUNE_API_TOKEN=\"your_api_token\"\n",
-      "env: NEPTUNE_PROJECT=\"workspace_name/project_name\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set environment variable for the API token\n",
-    "%env NEPTUNE_API_TOKEN = \"your_api_token\"\n",
-    "%env NEPTUNE_PROJECT = \"workspace_name/project_name\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 39f5bb94282d3dfc2fe5a2ce417b7caec6241f56 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 11:35:19 +0100
Subject: [PATCH 005/125] chore: update the introduction to be more foundation
 model training orientated

---
 .../pytorch/pytorch_image_classification.ipynb                  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 13af69e..667f8e2 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -8,6 +8,8 @@
     "\n",
     "Introduction\n",
     "\n",
+    "See how Neptune Scale can be used for foundation model traning when you are required to track a large number of metrics across your transformers architecture. \n",
+    "\n",
     "This guide will show you how to:\n",
     "- Initialize the Neptune Run object and log configuration parameters\n",
     "- Log standard loss and accuracy metrics to Neptune\n",

From 69fdfbafa4b36f39699b4b18ce8774ad7d57fed4 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 11:42:06 +0100
Subject: [PATCH 006/125] chore: update dataset section with a better
 description

---
 .../pytorch/pytorch_image_classification.ipynb   | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 667f8e2..91cdcb1 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,9 +97,17 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download and transform the data for training\n",
+    "In this example, we will be using the MINST dataset as part of the PyTorch library for illustration. We create a train, validation and test dataset and apply a transformation. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +126,7 @@
     "# DataLoader for training, validation, and testing\n",
     "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
     "val_loader = DataLoader(val_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n",
-    "test_loader = DataLoader(test_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n"
+    "test_loader = DataLoader(test_dataset, batch_size=params[\"batch_size\"], shuffle=False)"
    ]
   },
   {

From 1cde2871349e639ee7e9fe0589abbd6a7f441b5d Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 26 Feb 2025 14:13:46 +0100
Subject: [PATCH 007/125] refactor: update training loop where grads, norms and
 activations are tracked

---
 .../pytorch_image_classification.ipynb        | 83 ++++++++++++-------
 1 file changed, 55 insertions(+), 28 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 91cdcb1..4e94085 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,10 @@
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
     "\n",
-    "run = Run(run_id=f\"pytorch-{uuid4()}\")\n",
+    "run = Run(\n",
+    "    project = \"leo/pytorch-tutorial\",\n",
+    "    run_id=f\"pytorch-{uuid4()}\"\n",
+    "    )\n",
     "\n",
     "run.log_configs(\n",
     "    {\n",
@@ -285,11 +288,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [1/5] Training complete. Loss: 2.3020, Accuracy: 10.98%\n",
+      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.1060420349240303, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.05648726597428322, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 5.867332397713199e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.031105784699320793, 'fc3.bias': 0.09331735223531723}\n",
+      "Epoch [2/5] Training complete. Loss: 2.3023, Accuracy: 10.89%\n",
+      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10444235801696777, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.06586454063653946, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 2.2351742678949904e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.028597360476851463, 'fc3.bias': 0.08579207956790924}\n",
+      "Epoch [3/5] Training complete. Loss: 2.3024, Accuracy: 11.08%\n",
+      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10336797684431076, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.07147954404354095, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 4.842877210364804e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.034048620611429214, 'fc3.bias': 0.10214585065841675}\n",
+      "Epoch [4/5] Training complete. Loss: 2.3023, Accuracy: 11.11%\n",
+      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10569452494382858, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.08372259140014648, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 2.607703164514419e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.038734495639801025, 'fc3.bias': 0.11620348691940308}\n",
+      "Epoch [5/5] Training complete. Loss: 2.3026, Accuracy: 11.02%\n",
+      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10467425733804703, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.0657070130109787, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 1.1175870895385742e-08, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.03915564715862274, 'fc3.bias': 0.11746694147586823}\n",
+      "Testing complete. Loss: 2.3026, Accuracy: 10.32%\n"
+     ]
+    }
+   ],
    "source": [
     "# Training loop\n",
+    "activation_dict_mean = {}\n",
+    "activation_dict_std = {}\n",
+    "params_dict_std = {}\n",
+    "params_dict_mean = {}\n",
+    "grad_norms = {}\n",
+    "\n",
     "num_epochs = params[\"epochs\"]\n",
     "step_counter = 0\n",
     "for epoch in range(num_epochs):\n",
@@ -325,11 +352,11 @@
     "        # Print loss and accuracy for each batch (step)\n",
     "        #if (batch_idx + 1) % 5 == 0:  # Every 5 steps\n",
     "        batch_accuracy = 100 * correct_preds / total_preds\n",
-    "        print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\n",
+    "        # print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\n",
     "                \n",
     "        # Validation step per training step\n",
     "        val_loss, val_accuracy = evaluate(model, val_loader)  # Evaluate after each step\n",
-    "        print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\n",
+    "        # print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\n",
     "\n",
     "        run.log_metrics(\n",
     "            data = {\n",
@@ -346,27 +373,27 @@
     "    train_accuracy = 100 * correct_preds / total_preds\n",
     "    print(f\"Epoch [{epoch+1}/{num_epochs}] Training complete. Loss: {epoch_loss / len(train_loader):.4f}, Accuracy: {train_accuracy:.2f}%\")\n",
     "\n",
-    "    # Track activations and gradients per layer\n",
-    "    activation_dict_mean = {\n",
-    "        f\"layers/layer_{name}/activation_mean\": activation.mean().item() for name, activation in model.get_activations().items()\n",
-    "        }\n",
-    "    \n",
-    "    activation_dict_std = {\n",
-    "        f\"layers/layer_{name}/activation_std\": activation.std().item() for name, activation in model.get_activations().items()\n",
-    "    }\n",
+    "    # Track activations\n",
+    "    for name, activation in model.get_activations().items():\n",
+    "        activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation.mean().item()\n",
+    "        activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation.std().item()\n",
+    "\n",
+    "    # Track gradients\n",
+    "    for name, param in model.named_parameters():\n",
+    "        params_dict_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
+    "        params_dict_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
+    "\n",
+    "    # Track gradient norms per layer\n",
+    "    for param in model.parameters():\n",
+    "        if param.grad is not None:\n",
+    "            grad_norms[f\"{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
     "\n",
-    "    params_dict_std = {\n",
-    "            f\"layers/layer_{name.split(\".\")[0]}/{name.split(\".\")[1]}_std\": param.grad.std().item() for name, param in model.named_parameters()\n",
-    "        }\n",
-    "    \n",
-    "    params_dict_mean = {\n",
-    "            f\"layers/layer_{name.split(\".\")[0]}/{name.split(\".\")[1]}_mean\": param.grad.mean().item() for name, param in model.named_parameters()\n",
-    "        }\n",
     "    \n",
     "    layers_dict = {**activation_dict_mean, \n",
     "                   **activation_dict_std,\n",
     "                   **params_dict_mean,\n",
-    "                   **params_dict_std\n",
+    "                   **params_dict_std,\n",
+    "                   **grad_norms\n",
     "                   }\n",
     "    print(layers_dict)\n",
     "\n",
@@ -394,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {

From 2da28f4e2a10c6451ba3f4dc41be0c92efedd1cc Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 27 Feb 2025 11:28:25 +0100
Subject: [PATCH 008/125] refactor: update batch size and edit gradient norm
 logging code

---
 .../pytorch/pytorch_image_classification.ipynb    | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 4e94085..75bd712 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -81,13 +81,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 512,\n",
+    "    \"batch_size\": 256,\n",
     "    \"learning_rate\": 0.05,\n",
     "    \"epochs\": 5, \n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,16 +378,11 @@
     "        activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation.mean().item()\n",
     "        activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation.std().item()\n",
     "\n",
-    "    # Track gradients\n",
+    "    # Track gradients and norms per layer at each epoch\n",
     "    for name, param in model.named_parameters():\n",
     "        params_dict_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
     "        params_dict_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "\n",
-    "    # Track gradient norms per layer\n",
-    "    for param in model.parameters():\n",
-    "        if param.grad is not None:\n",
-    "            grad_norms[f\"{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
-    "\n",
+    "        grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
     "    \n",
     "    layers_dict = {**activation_dict_mean, \n",
     "                   **activation_dict_std,\n",

From 123fea2d201545677ce12a5ae1275fa126b6c861 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 27 Feb 2025 11:30:07 +0100
Subject: [PATCH 009/125] chore: add data file to ignore for pytorch example

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 0022e84..2a6943c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 # Data files
 /how-to-guides/hpo/**/mnist
+/integrations-and-supported-tools/pytorch/data
\ No newline at end of file

From 86b6d5dea9587a48f2e7caac90b69ad664c503b6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 27 Feb 2025 12:44:32 +0100
Subject: [PATCH 010/125] refactor: update model architecture layers and update
 training loop

---
 .../pytorch_image_classification.ipynb        | 54 +++++++++++--------
 1 file changed, 31 insertions(+), 23 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 75bd712..db925e0 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -168,20 +168,26 @@
     "        super(SimpleNN, self).__init__()\n",
     "        # Define layers (increase number of layers)\n",
     "        self.fc1 = nn.Linear(params[\"input_size\"], params[\"input_features\"]) \n",
-    "        self.fc2 = nn.Linear(params[\"input_features\"], 64)\n",
-    "        self.fc3 = nn.Linear(64, params[\"n_classes\"])      # Output layer (10 classes for MNIST)\n",
+    "        self.fc2 = nn.Linear(params[\"input_features\"], 150)\n",
+    "        self.fc3 = nn.Linear(150, 100)\n",
+    "        self.fc4 = nn.Linear(100, 64)\n",
+    "        self.fc5 = nn.Linear(64, params[\"n_classes\"])      # Output layer (10 classes for MNIST)\n",
     "\n",
     "        # Registering hooks to track activations\n",
     "        self.hooks = []\n",
     "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
     "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
     "        self.hooks.append(self.fc3.register_forward_hook(self.save_activation(\"fc3\")))\n",
+    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc4\")))\n",
+    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc5\")))\n",
     "\n",
     "    def forward(self, x):\n",
     "        x = x.view(-1, params[\"input_size\"])  # Flatten the input image (28x28)\n",
     "        x = torch.relu(self.fc1(x))  # Apply ReLU activation\n",
     "        x = torch.relu(self.fc2(x))  # Apply ReLU activation\n",
-    "        x = self.fc3(x)  # Output layer\n",
+    "        x = torch.relu(self.fc3(x))  # Apply ReLU activation\n",
+    "        x = torch.relu(self.fc4(x))  # Apply ReLU activation\n",
+    "        x = self.fc5(x)  # Output layer\n",
     "        return x\n",
     "    \n",
     "        # Function to save activations\n",
@@ -214,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -253,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,24 +294,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch [1/5] Training complete. Loss: 2.3020, Accuracy: 10.98%\n",
-      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.1060420349240303, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.05648726597428322, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 5.867332397713199e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.031105784699320793, 'fc3.bias': 0.09331735223531723}\n",
-      "Epoch [2/5] Training complete. Loss: 2.3023, Accuracy: 10.89%\n",
-      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10444235801696777, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.06586454063653946, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 2.2351742678949904e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.028597360476851463, 'fc3.bias': 0.08579207956790924}\n",
-      "Epoch [3/5] Training complete. Loss: 2.3024, Accuracy: 11.08%\n",
-      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10336797684431076, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.07147954404354095, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 4.842877210364804e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.034048620611429214, 'fc3.bias': 0.10214585065841675}\n",
-      "Epoch [4/5] Training complete. Loss: 2.3023, Accuracy: 11.11%\n",
-      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10569452494382858, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.08372259140014648, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 2.607703164514419e-09, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.038734495639801025, 'fc3.bias': 0.11620348691940308}\n",
-      "Epoch [5/5] Training complete. Loss: 2.3026, Accuracy: 11.02%\n",
-      "{'layers/layer_fc1/activation_mean': -154.4237060546875, 'layers/layer_fc2/activation_mean': -0.2948070466518402, 'layers/layer_fc3/activation_mean': 0.10467425733804703, 'layers/layer_fc1/activation_std': 38.38949966430664, 'layers/layer_fc2/activation_std': 0.1372535228729248, 'layers/layer_fc3/activation_std': 0.0657070130109787, 'layers/layer_fc1.weight_mean': 0.0, 'layers/layer_fc1.bias_mean': 0.0, 'layers/layer_fc2.weight_mean': 0.0, 'layers/layer_fc2.bias_mean': 0.0, 'layers/layer_fc3.weight_mean': 0.0, 'layers/layer_fc3.bias_mean': 1.1175870895385742e-08, 'layers/layer_fc1.weight_std': 0.0, 'layers/layer_fc1.bias_std': 0.0, 'layers/layer_fc2.weight_std': 0.0, 'layers/layer_fc2.bias_std': 0.0, 'layers/layer_fc3.weight_std': 0.0, 'layers/layer_fc3.bias_std': 0.03915564715862274, 'fc3.bias': 0.11746694147586823}\n",
-      "Testing complete. Loss: 2.3026, Accuracy: 10.32%\n"
+      "Epoch [1/5] Training complete. Loss: 2.4092, Accuracy: 37.12%\n",
+      "{'layers/layer_fc1/activation_mean': -162.4604949951172, 'layers/layer_fc4/activation_mean': -162.4604949951172, 'layers/layer_fc5/activation_mean': -162.4604949951172, 'layers/layer_fc2/activation_mean': -12.759440422058105, 'layers/layer_fc3/activation_mean': -5.810527801513672, 'layers/layer_fc1/activation_std': 39.21321487426758, 'layers/layer_fc4/activation_std': 39.21321487426758, 'layers/layer_fc5/activation_std': 39.21321487426758, 'layers/layer_fc2/activation_std': 10.599294662475586, 'layers/layer_fc3/activation_std': 7.578292369842529, 'layers/layer_fc1.weight_mean': 4.031595744891092e-05, 'layers/layer_fc1.bias_mean': -5.974042505840771e-05, 'layers/layer_fc2.weight_mean': -1.2967424481757917e-05, 'layers/layer_fc2.bias_mean': -0.00016122810484375805, 'layers/layer_fc3.weight_mean': 6.324012247205246e-06, 'layers/layer_fc3.bias_mean': 0.00010194708738708869, 'layers/layer_fc4.weight_mean': -0.00013273996592033654, 'layers/layer_fc4.bias_mean': 0.0001236710959346965, 'layers/layer_fc5.weight_mean': -5.18019738215969e-10, 'layers/layer_fc5.bias_mean': -6.053596512956005e-10, 'layers/layer_fc1.weight_std': 0.00062244210857898, 'layers/layer_fc1.bias_std': 0.0006770123727619648, 'layers/layer_fc2.weight_std': 0.0038620077539235353, 'layers/layer_fc2.bias_std': 0.0019892167765647173, 'layers/layer_fc3.weight_std': 0.003400410758331418, 'layers/layer_fc3.bias_std': 0.004144275560975075, 'layers/layer_fc4.weight_std': 0.0041240728460252285, 'layers/layer_fc4.bias_std': 0.004583150614053011, 'layers/layer_fc5.weight_std': 0.019711505621671677, 'layers/layer_fc5.bias_std': 0.02345249615609646, 'grad_norm/fc1.weight': 0.2794375717639923, 'grad_norm/fc1.bias': 0.010853193700313568, 'grad_norm/fc2.weight': 0.756790280342102, 'grad_norm/fc2.bias': 0.024361643940210342, 'grad_norm/fc3.weight': 0.4164503812789917, 'grad_norm/fc3.bias': 0.041247621178627014, 'grad_norm/fc4.weight': 0.3300709128379822, 'grad_norm/fc4.bias': 0.036391083151102066, 'grad_norm/fc5.weight': 0.49827632308006287, 'grad_norm/fc5.bias': 0.07035749405622482}\n",
+      "Epoch [2/5] Training complete. Loss: 0.9144, Accuracy: 67.32%\n",
+      "{'layers/layer_fc1/activation_mean': -162.39700317382812, 'layers/layer_fc4/activation_mean': -162.39700317382812, 'layers/layer_fc5/activation_mean': -162.39700317382812, 'layers/layer_fc2/activation_mean': -17.86925506591797, 'layers/layer_fc3/activation_mean': -7.695600986480713, 'layers/layer_fc1/activation_std': 39.54920959472656, 'layers/layer_fc4/activation_std': 39.54920959472656, 'layers/layer_fc5/activation_std': 39.54920959472656, 'layers/layer_fc2/activation_std': 12.200335502624512, 'layers/layer_fc3/activation_std': 7.782861709594727, 'layers/layer_fc1.weight_mean': -3.351208579260856e-05, 'layers/layer_fc1.bias_mean': 4.919838102068752e-05, 'layers/layer_fc2.weight_mean': 3.6362267564982176e-05, 'layers/layer_fc2.bias_mean': 0.00021275453036651015, 'layers/layer_fc3.weight_mean': 1.2260925359441899e-05, 'layers/layer_fc3.bias_mean': 0.0003240771184209734, 'layers/layer_fc4.weight_mean': -0.00010540984658291563, 'layers/layer_fc4.bias_mean': -0.0003542335471138358, 'layers/layer_fc5.weight_mean': 1.9608706325335845e-10, 'layers/layer_fc5.bias_mean': 1.8626451769865326e-10, 'layers/layer_fc1.weight_std': 0.0006732655456289649, 'layers/layer_fc1.bias_std': 0.0007651570485904813, 'layers/layer_fc2.weight_std': 0.007791644427925348, 'layers/layer_fc2.bias_std': 0.004374559037387371, 'layers/layer_fc3.weight_std': 0.004076257348060608, 'layers/layer_fc3.bias_std': 0.0062528871931135654, 'layers/layer_fc4.weight_std': 0.00545633165165782, 'layers/layer_fc4.bias_std': 0.006939326878637075, 'layers/layer_fc5.weight_std': 0.016751136630773544, 'layers/layer_fc5.bias_std': 0.014692811295390129, 'grad_norm/fc1.weight': 0.3019956946372986, 'grad_norm/fc1.bias': 0.012243907898664474, 'grad_norm/fc2.weight': 1.5268410444259644, 'grad_norm/fc2.bias': 0.05346183478832245, 'grad_norm/fc3.weight': 0.49922317266464233, 'grad_norm/fc3.bias': 0.062299784272909164, 'grad_norm/fc4.weight': 0.43655386567115784, 'grad_norm/fc4.bias': 0.055152054876089096, 'grad_norm/fc5.weight': 0.4234427511692047, 'grad_norm/fc5.bias': 0.044078435748815536}\n",
+      "Epoch [3/5] Training complete. Loss: 0.7789, Accuracy: 73.94%\n",
+      "{'layers/layer_fc1/activation_mean': -162.37669372558594, 'layers/layer_fc4/activation_mean': -162.37669372558594, 'layers/layer_fc5/activation_mean': -162.37669372558594, 'layers/layer_fc2/activation_mean': -18.648223876953125, 'layers/layer_fc3/activation_mean': -7.7249956130981445, 'layers/layer_fc1/activation_std': 39.64369201660156, 'layers/layer_fc4/activation_std': 39.64369201660156, 'layers/layer_fc5/activation_std': 39.64369201660156, 'layers/layer_fc2/activation_std': 13.20457935333252, 'layers/layer_fc3/activation_std': 8.523029327392578, 'layers/layer_fc1.weight_mean': -4.384123894851655e-05, 'layers/layer_fc1.bias_mean': 5.376951958169229e-05, 'layers/layer_fc2.weight_mean': 0.00012754423369187862, 'layers/layer_fc2.bias_mean': 0.0002990294306073338, 'layers/layer_fc3.weight_mean': 7.330457447096705e-05, 'layers/layer_fc3.bias_mean': 0.0008429083391092718, 'layers/layer_fc4.weight_mean': -0.00011236544378334656, 'layers/layer_fc4.bias_mean': -0.0007185321301221848, 'layers/layer_fc5.weight_mean': -1.0186340659856796e-10, 'layers/layer_fc5.bias_mean': 7.450580707946131e-10, 'layers/layer_fc1.weight_std': 0.000986010069027543, 'layers/layer_fc1.bias_std': 0.001086454140022397, 'layers/layer_fc2.weight_std': 0.012911902740597725, 'layers/layer_fc2.bias_std': 0.0031231597531586885, 'layers/layer_fc3.weight_std': 0.006555264815688133, 'layers/layer_fc3.bias_std': 0.006345531903207302, 'layers/layer_fc4.weight_std': 0.008043059147894382, 'layers/layer_fc4.bias_std': 0.0076463837176561356, 'layers/layer_fc5.weight_std': 0.038150131702423096, 'layers/layer_fc5.bias_std': 0.0253444854170084, 'grad_norm/fc1.weight': 0.4421679377555847, 'grad_norm/fc1.bias': 0.017370598390698433, 'grad_norm/fc2.weight': 2.5302963256835938, 'grad_norm/fc2.bias': 0.038298528641462326, 'grad_norm/fc3.weight': 0.8028761744499207, 'grad_norm/fc3.bias': 0.06369742006063461, 'grad_norm/fc4.weight': 0.6434571743011475, 'grad_norm/fc4.bias': 0.06096290051937103, 'grad_norm/fc5.weight': 0.9643760919570923, 'grad_norm/fc5.bias': 0.07603345066308975}\n",
+      "Epoch [4/5] Training complete. Loss: 0.7422, Accuracy: 75.72%\n",
+      "{'layers/layer_fc1/activation_mean': -162.26683044433594, 'layers/layer_fc4/activation_mean': -162.26683044433594, 'layers/layer_fc5/activation_mean': -162.26683044433594, 'layers/layer_fc2/activation_mean': -24.927921295166016, 'layers/layer_fc3/activation_mean': -10.208073616027832, 'layers/layer_fc1/activation_std': 40.17057800292969, 'layers/layer_fc4/activation_std': 40.17057800292969, 'layers/layer_fc5/activation_std': 40.17057800292969, 'layers/layer_fc2/activation_std': 16.805315017700195, 'layers/layer_fc3/activation_std': 9.891535758972168, 'layers/layer_fc1.weight_mean': -5.1961251301690936e-05, 'layers/layer_fc1.bias_mean': 6.783485878258944e-05, 'layers/layer_fc2.weight_mean': 0.00011843817628687248, 'layers/layer_fc2.bias_mean': 0.0007921061478555202, 'layers/layer_fc3.weight_mean': 0.0001514973264420405, 'layers/layer_fc3.bias_mean': 0.0010678424732759595, 'layers/layer_fc4.weight_mean': 1.0874953659367748e-05, 'layers/layer_fc4.bias_mean': -4.5362801756709814e-05, 'layers/layer_fc5.weight_mean': 1.8471837948119685e-10, 'layers/layer_fc5.bias_mean': 5.587935669737476e-10, 'layers/layer_fc1.weight_std': 0.0006585291703231633, 'layers/layer_fc1.bias_std': 0.0007209584582597017, 'layers/layer_fc2.weight_std': 0.010477228090167046, 'layers/layer_fc2.bias_std': 0.005132471676915884, 'layers/layer_fc3.weight_std': 0.008328537456691265, 'layers/layer_fc3.bias_std': 0.008772918954491615, 'layers/layer_fc4.weight_std': 0.005717130843549967, 'layers/layer_fc4.bias_std': 0.005424214527010918, 'layers/layer_fc5.weight_std': 0.031180905178189278, 'layers/layer_fc5.bias_std': 0.030189601704478264, 'grad_norm/fc1.weight': 0.29593732953071594, 'grad_norm/fc1.bias': 0.011563831008970737, 'grad_norm/fc2.weight': 2.053213596343994, 'grad_norm/fc2.bias': 0.06339646875858307, 'grad_norm/fc3.weight': 1.0201679468154907, 'grad_norm/fc3.bias': 0.08794017881155014, 'grad_norm/fc4.weight': 0.45733556151390076, 'grad_norm/fc4.bias': 0.04305489733815193, 'grad_norm/fc5.weight': 0.7882049083709717, 'grad_norm/fc5.bias': 0.09056880325078964}\n",
+      "Epoch [5/5] Training complete. Loss: 0.7054, Accuracy: 76.78%\n",
+      "{'layers/layer_fc1/activation_mean': -162.22689819335938, 'layers/layer_fc4/activation_mean': -162.22689819335938, 'layers/layer_fc5/activation_mean': -162.22689819335938, 'layers/layer_fc2/activation_mean': -26.883211135864258, 'layers/layer_fc3/activation_mean': -14.033665657043457, 'layers/layer_fc1/activation_std': 40.35655975341797, 'layers/layer_fc4/activation_std': 40.35655975341797, 'layers/layer_fc5/activation_std': 40.35655975341797, 'layers/layer_fc2/activation_std': 18.789628982543945, 'layers/layer_fc3/activation_std': 13.676512718200684, 'layers/layer_fc1.weight_mean': 1.5473866369575262e-05, 'layers/layer_fc1.bias_mean': -1.7887834474095143e-05, 'layers/layer_fc2.weight_mean': -0.00011709203681675717, 'layers/layer_fc2.bias_mean': -0.00017746593221090734, 'layers/layer_fc3.weight_mean': -9.6314717666246e-05, 'layers/layer_fc3.bias_mean': -0.0003019513678736985, 'layers/layer_fc4.weight_mean': 7.599956006743014e-05, 'layers/layer_fc4.bias_mean': 7.583803380839527e-06, 'layers/layer_fc5.weight_mean': 1.14232533154901e-10, 'layers/layer_fc5.bias_mean': 0.0, 'layers/layer_fc1.weight_std': 0.0006393238436430693, 'layers/layer_fc1.bias_std': 0.0007226161542348564, 'layers/layer_fc2.weight_std': 0.01606857031583786, 'layers/layer_fc2.bias_std': 0.002762269927188754, 'layers/layer_fc3.weight_std': 0.009842080064117908, 'layers/layer_fc3.bias_std': 0.0029760582838207483, 'layers/layer_fc4.weight_std': 0.008030076511204243, 'layers/layer_fc4.bias_std': 0.005211194045841694, 'layers/layer_fc5.weight_std': 0.02980860136449337, 'layers/layer_fc5.bias_std': 0.024154536426067352, 'grad_norm/fc1.weight': 0.2865002751350403, 'grad_norm/fc1.bias': 0.01154280360788107, 'grad_norm/fc2.weight': 3.1488263607025146, 'grad_norm/fc2.bias': 0.03378778323531151, 'grad_norm/fc3.weight': 1.2054210901260376, 'grad_norm/fc3.bias': 0.02976495958864689, 'grad_norm/fc4.weight': 0.6423847079277039, 'grad_norm/fc4.bias': 0.041362613439559937, 'grad_norm/fc5.weight': 0.7535152435302734, 'grad_norm/fc5.bias': 0.07246360927820206}\n",
+      "Testing complete. Loss: 0.6699, Accuracy: 78.28%\n"
      ]
     }
    ],
@@ -338,11 +344,12 @@
     "        \n",
     "        # Compute the loss\n",
     "        loss = criterion(output, target)\n",
-    "        epoch_loss += loss.item()\n",
     "        \n",
     "        # Backward pass and optimization\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
+    "\n",
+    "        epoch_loss += loss.item()\n",
     "        \n",
     "        # Calculate accuracy\n",
     "        _, predicted = torch.max(output.data, 1)\n",
@@ -380,9 +387,10 @@
     "\n",
     "    # Track gradients and norms per layer at each epoch\n",
     "    for name, param in model.named_parameters():\n",
-    "        params_dict_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
-    "        params_dict_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "        grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
+    "        if param is not None:\n",
+    "            params_dict_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
+    "            params_dict_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
+    "            grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
     "    \n",
     "    layers_dict = {**activation_dict_mean, \n",
     "                   **activation_dict_std,\n",
@@ -416,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {

From a39077aeb688faefe39d6ee44777c67615b23a40 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 27 Feb 2025 15:42:26 +0100
Subject: [PATCH 011/125] refactor: update accuracy calculation to not output
 the percentage

---
 .../pytorch_image_classification.ipynb        | 75 ++++++++++++-------
 1 file changed, 48 insertions(+), 27 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index db925e0..69f68c7 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -54,7 +54,10 @@
    "source": [
     "# TODO - update config to include model architecture\n",
     "# TODO - Add more hyperparameters\n",
-    "# TODO - Add additional logging metrics (weights, gradients, activations, etc.)"
+    "# TODO - look at CNN layers\n",
+    "# TODO - output and log the model architecture\n",
+    "# TODO - check loss and accuracy calculations\n",
+    "# TODO - clean up the evaluation function to exclude tracking gradients"
    ]
   },
   {
@@ -81,14 +84,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
     "    \"batch_size\": 256,\n",
-    "    \"learning_rate\": 0.05,\n",
+    "    \"learning_rate\": 0.01,\n",
     "    \"epochs\": 5, \n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
@@ -131,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -168,10 +171,10 @@
     "        super(SimpleNN, self).__init__()\n",
     "        # Define layers (increase number of layers)\n",
     "        self.fc1 = nn.Linear(params[\"input_size\"], params[\"input_features\"]) \n",
-    "        self.fc2 = nn.Linear(params[\"input_features\"], 150)\n",
-    "        self.fc3 = nn.Linear(150, 100)\n",
-    "        self.fc4 = nn.Linear(100, 64)\n",
-    "        self.fc5 = nn.Linear(64, params[\"n_classes\"])      # Output layer (10 classes for MNIST)\n",
+    "        self.fc2 = nn.Linear(params[\"input_features\"], 512)\n",
+    "        self.fc3 = nn.Linear(512, 256)\n",
+    "        self.fc4 = nn.Linear(256, 128)\n",
+    "        self.fc5 = nn.Linear(128, params[\"n_classes\"])      # Output layer (10 classes for MNIST)\n",
     "\n",
     "        # Registering hooks to track activations\n",
     "        self.hooks = []\n",
@@ -203,7 +206,7 @@
     "        self.activations = {}\n",
     "\n",
     "# Instantiate the model, loss function, and optimizer\n",
-    "# model = SimpleCNN()\n",
+    "#model = SimpleCNN()\n",
     "model = SimpleNN()\n",
     "criterion = nn.CrossEntropyLoss()  # Loss function\n",
     "\n",
@@ -220,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +249,7 @@
     "            total_preds += target.size(0)\n",
     "            correct_preds += (predicted == target).sum().item()\n",
     "    \n",
-    "    accuracy = 100 * correct_preds / total_preds\n",
+    "    accuracy = correct_preds / total_preds\n",
     "    return epoch_loss / len(data_loader), accuracy\n"
    ]
   },
@@ -259,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +280,8 @@
     "        \"config/learning_rate\": params[\"learning_rate\"],\n",
     "        \"config/optimizer\": params[\"optimizer\"],\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
-    "        \"config/epochs\": params[\"epochs\"]\n",
+    "        \"config/epochs\": params[\"epochs\"],\n",
+    "        \"config/input_size\": params[\"input_size\"]\n",
     "    }\n",
     ")\n",
     "\n",
@@ -294,24 +298,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch [1/5] Training complete. Loss: 2.4092, Accuracy: 37.12%\n",
-      "{'layers/layer_fc1/activation_mean': -162.4604949951172, 'layers/layer_fc4/activation_mean': -162.4604949951172, 'layers/layer_fc5/activation_mean': -162.4604949951172, 'layers/layer_fc2/activation_mean': -12.759440422058105, 'layers/layer_fc3/activation_mean': -5.810527801513672, 'layers/layer_fc1/activation_std': 39.21321487426758, 'layers/layer_fc4/activation_std': 39.21321487426758, 'layers/layer_fc5/activation_std': 39.21321487426758, 'layers/layer_fc2/activation_std': 10.599294662475586, 'layers/layer_fc3/activation_std': 7.578292369842529, 'layers/layer_fc1.weight_mean': 4.031595744891092e-05, 'layers/layer_fc1.bias_mean': -5.974042505840771e-05, 'layers/layer_fc2.weight_mean': -1.2967424481757917e-05, 'layers/layer_fc2.bias_mean': -0.00016122810484375805, 'layers/layer_fc3.weight_mean': 6.324012247205246e-06, 'layers/layer_fc3.bias_mean': 0.00010194708738708869, 'layers/layer_fc4.weight_mean': -0.00013273996592033654, 'layers/layer_fc4.bias_mean': 0.0001236710959346965, 'layers/layer_fc5.weight_mean': -5.18019738215969e-10, 'layers/layer_fc5.bias_mean': -6.053596512956005e-10, 'layers/layer_fc1.weight_std': 0.00062244210857898, 'layers/layer_fc1.bias_std': 0.0006770123727619648, 'layers/layer_fc2.weight_std': 0.0038620077539235353, 'layers/layer_fc2.bias_std': 0.0019892167765647173, 'layers/layer_fc3.weight_std': 0.003400410758331418, 'layers/layer_fc3.bias_std': 0.004144275560975075, 'layers/layer_fc4.weight_std': 0.0041240728460252285, 'layers/layer_fc4.bias_std': 0.004583150614053011, 'layers/layer_fc5.weight_std': 0.019711505621671677, 'layers/layer_fc5.bias_std': 0.02345249615609646, 'grad_norm/fc1.weight': 0.2794375717639923, 'grad_norm/fc1.bias': 0.010853193700313568, 'grad_norm/fc2.weight': 0.756790280342102, 'grad_norm/fc2.bias': 0.024361643940210342, 'grad_norm/fc3.weight': 0.4164503812789917, 'grad_norm/fc3.bias': 0.041247621178627014, 'grad_norm/fc4.weight': 0.3300709128379822, 'grad_norm/fc4.bias': 0.036391083151102066, 'grad_norm/fc5.weight': 0.49827632308006287, 'grad_norm/fc5.bias': 0.07035749405622482}\n",
-      "Epoch [2/5] Training complete. Loss: 0.9144, Accuracy: 67.32%\n",
-      "{'layers/layer_fc1/activation_mean': -162.39700317382812, 'layers/layer_fc4/activation_mean': -162.39700317382812, 'layers/layer_fc5/activation_mean': -162.39700317382812, 'layers/layer_fc2/activation_mean': -17.86925506591797, 'layers/layer_fc3/activation_mean': -7.695600986480713, 'layers/layer_fc1/activation_std': 39.54920959472656, 'layers/layer_fc4/activation_std': 39.54920959472656, 'layers/layer_fc5/activation_std': 39.54920959472656, 'layers/layer_fc2/activation_std': 12.200335502624512, 'layers/layer_fc3/activation_std': 7.782861709594727, 'layers/layer_fc1.weight_mean': -3.351208579260856e-05, 'layers/layer_fc1.bias_mean': 4.919838102068752e-05, 'layers/layer_fc2.weight_mean': 3.6362267564982176e-05, 'layers/layer_fc2.bias_mean': 0.00021275453036651015, 'layers/layer_fc3.weight_mean': 1.2260925359441899e-05, 'layers/layer_fc3.bias_mean': 0.0003240771184209734, 'layers/layer_fc4.weight_mean': -0.00010540984658291563, 'layers/layer_fc4.bias_mean': -0.0003542335471138358, 'layers/layer_fc5.weight_mean': 1.9608706325335845e-10, 'layers/layer_fc5.bias_mean': 1.8626451769865326e-10, 'layers/layer_fc1.weight_std': 0.0006732655456289649, 'layers/layer_fc1.bias_std': 0.0007651570485904813, 'layers/layer_fc2.weight_std': 0.007791644427925348, 'layers/layer_fc2.bias_std': 0.004374559037387371, 'layers/layer_fc3.weight_std': 0.004076257348060608, 'layers/layer_fc3.bias_std': 0.0062528871931135654, 'layers/layer_fc4.weight_std': 0.00545633165165782, 'layers/layer_fc4.bias_std': 0.006939326878637075, 'layers/layer_fc5.weight_std': 0.016751136630773544, 'layers/layer_fc5.bias_std': 0.014692811295390129, 'grad_norm/fc1.weight': 0.3019956946372986, 'grad_norm/fc1.bias': 0.012243907898664474, 'grad_norm/fc2.weight': 1.5268410444259644, 'grad_norm/fc2.bias': 0.05346183478832245, 'grad_norm/fc3.weight': 0.49922317266464233, 'grad_norm/fc3.bias': 0.062299784272909164, 'grad_norm/fc4.weight': 0.43655386567115784, 'grad_norm/fc4.bias': 0.055152054876089096, 'grad_norm/fc5.weight': 0.4234427511692047, 'grad_norm/fc5.bias': 0.044078435748815536}\n",
-      "Epoch [3/5] Training complete. Loss: 0.7789, Accuracy: 73.94%\n",
-      "{'layers/layer_fc1/activation_mean': -162.37669372558594, 'layers/layer_fc4/activation_mean': -162.37669372558594, 'layers/layer_fc5/activation_mean': -162.37669372558594, 'layers/layer_fc2/activation_mean': -18.648223876953125, 'layers/layer_fc3/activation_mean': -7.7249956130981445, 'layers/layer_fc1/activation_std': 39.64369201660156, 'layers/layer_fc4/activation_std': 39.64369201660156, 'layers/layer_fc5/activation_std': 39.64369201660156, 'layers/layer_fc2/activation_std': 13.20457935333252, 'layers/layer_fc3/activation_std': 8.523029327392578, 'layers/layer_fc1.weight_mean': -4.384123894851655e-05, 'layers/layer_fc1.bias_mean': 5.376951958169229e-05, 'layers/layer_fc2.weight_mean': 0.00012754423369187862, 'layers/layer_fc2.bias_mean': 0.0002990294306073338, 'layers/layer_fc3.weight_mean': 7.330457447096705e-05, 'layers/layer_fc3.bias_mean': 0.0008429083391092718, 'layers/layer_fc4.weight_mean': -0.00011236544378334656, 'layers/layer_fc4.bias_mean': -0.0007185321301221848, 'layers/layer_fc5.weight_mean': -1.0186340659856796e-10, 'layers/layer_fc5.bias_mean': 7.450580707946131e-10, 'layers/layer_fc1.weight_std': 0.000986010069027543, 'layers/layer_fc1.bias_std': 0.001086454140022397, 'layers/layer_fc2.weight_std': 0.012911902740597725, 'layers/layer_fc2.bias_std': 0.0031231597531586885, 'layers/layer_fc3.weight_std': 0.006555264815688133, 'layers/layer_fc3.bias_std': 0.006345531903207302, 'layers/layer_fc4.weight_std': 0.008043059147894382, 'layers/layer_fc4.bias_std': 0.0076463837176561356, 'layers/layer_fc5.weight_std': 0.038150131702423096, 'layers/layer_fc5.bias_std': 0.0253444854170084, 'grad_norm/fc1.weight': 0.4421679377555847, 'grad_norm/fc1.bias': 0.017370598390698433, 'grad_norm/fc2.weight': 2.5302963256835938, 'grad_norm/fc2.bias': 0.038298528641462326, 'grad_norm/fc3.weight': 0.8028761744499207, 'grad_norm/fc3.bias': 0.06369742006063461, 'grad_norm/fc4.weight': 0.6434571743011475, 'grad_norm/fc4.bias': 0.06096290051937103, 'grad_norm/fc5.weight': 0.9643760919570923, 'grad_norm/fc5.bias': 0.07603345066308975}\n",
-      "Epoch [4/5] Training complete. Loss: 0.7422, Accuracy: 75.72%\n",
-      "{'layers/layer_fc1/activation_mean': -162.26683044433594, 'layers/layer_fc4/activation_mean': -162.26683044433594, 'layers/layer_fc5/activation_mean': -162.26683044433594, 'layers/layer_fc2/activation_mean': -24.927921295166016, 'layers/layer_fc3/activation_mean': -10.208073616027832, 'layers/layer_fc1/activation_std': 40.17057800292969, 'layers/layer_fc4/activation_std': 40.17057800292969, 'layers/layer_fc5/activation_std': 40.17057800292969, 'layers/layer_fc2/activation_std': 16.805315017700195, 'layers/layer_fc3/activation_std': 9.891535758972168, 'layers/layer_fc1.weight_mean': -5.1961251301690936e-05, 'layers/layer_fc1.bias_mean': 6.783485878258944e-05, 'layers/layer_fc2.weight_mean': 0.00011843817628687248, 'layers/layer_fc2.bias_mean': 0.0007921061478555202, 'layers/layer_fc3.weight_mean': 0.0001514973264420405, 'layers/layer_fc3.bias_mean': 0.0010678424732759595, 'layers/layer_fc4.weight_mean': 1.0874953659367748e-05, 'layers/layer_fc4.bias_mean': -4.5362801756709814e-05, 'layers/layer_fc5.weight_mean': 1.8471837948119685e-10, 'layers/layer_fc5.bias_mean': 5.587935669737476e-10, 'layers/layer_fc1.weight_std': 0.0006585291703231633, 'layers/layer_fc1.bias_std': 0.0007209584582597017, 'layers/layer_fc2.weight_std': 0.010477228090167046, 'layers/layer_fc2.bias_std': 0.005132471676915884, 'layers/layer_fc3.weight_std': 0.008328537456691265, 'layers/layer_fc3.bias_std': 0.008772918954491615, 'layers/layer_fc4.weight_std': 0.005717130843549967, 'layers/layer_fc4.bias_std': 0.005424214527010918, 'layers/layer_fc5.weight_std': 0.031180905178189278, 'layers/layer_fc5.bias_std': 0.030189601704478264, 'grad_norm/fc1.weight': 0.29593732953071594, 'grad_norm/fc1.bias': 0.011563831008970737, 'grad_norm/fc2.weight': 2.053213596343994, 'grad_norm/fc2.bias': 0.06339646875858307, 'grad_norm/fc3.weight': 1.0201679468154907, 'grad_norm/fc3.bias': 0.08794017881155014, 'grad_norm/fc4.weight': 0.45733556151390076, 'grad_norm/fc4.bias': 0.04305489733815193, 'grad_norm/fc5.weight': 0.7882049083709717, 'grad_norm/fc5.bias': 0.09056880325078964}\n",
-      "Epoch [5/5] Training complete. Loss: 0.7054, Accuracy: 76.78%\n",
-      "{'layers/layer_fc1/activation_mean': -162.22689819335938, 'layers/layer_fc4/activation_mean': -162.22689819335938, 'layers/layer_fc5/activation_mean': -162.22689819335938, 'layers/layer_fc2/activation_mean': -26.883211135864258, 'layers/layer_fc3/activation_mean': -14.033665657043457, 'layers/layer_fc1/activation_std': 40.35655975341797, 'layers/layer_fc4/activation_std': 40.35655975341797, 'layers/layer_fc5/activation_std': 40.35655975341797, 'layers/layer_fc2/activation_std': 18.789628982543945, 'layers/layer_fc3/activation_std': 13.676512718200684, 'layers/layer_fc1.weight_mean': 1.5473866369575262e-05, 'layers/layer_fc1.bias_mean': -1.7887834474095143e-05, 'layers/layer_fc2.weight_mean': -0.00011709203681675717, 'layers/layer_fc2.bias_mean': -0.00017746593221090734, 'layers/layer_fc3.weight_mean': -9.6314717666246e-05, 'layers/layer_fc3.bias_mean': -0.0003019513678736985, 'layers/layer_fc4.weight_mean': 7.599956006743014e-05, 'layers/layer_fc4.bias_mean': 7.583803380839527e-06, 'layers/layer_fc5.weight_mean': 1.14232533154901e-10, 'layers/layer_fc5.bias_mean': 0.0, 'layers/layer_fc1.weight_std': 0.0006393238436430693, 'layers/layer_fc1.bias_std': 0.0007226161542348564, 'layers/layer_fc2.weight_std': 0.01606857031583786, 'layers/layer_fc2.bias_std': 0.002762269927188754, 'layers/layer_fc3.weight_std': 0.009842080064117908, 'layers/layer_fc3.bias_std': 0.0029760582838207483, 'layers/layer_fc4.weight_std': 0.008030076511204243, 'layers/layer_fc4.bias_std': 0.005211194045841694, 'layers/layer_fc5.weight_std': 0.02980860136449337, 'layers/layer_fc5.bias_std': 0.024154536426067352, 'grad_norm/fc1.weight': 0.2865002751350403, 'grad_norm/fc1.bias': 0.01154280360788107, 'grad_norm/fc2.weight': 3.1488263607025146, 'grad_norm/fc2.bias': 0.03378778323531151, 'grad_norm/fc3.weight': 1.2054210901260376, 'grad_norm/fc3.bias': 0.02976495958864689, 'grad_norm/fc4.weight': 0.6423847079277039, 'grad_norm/fc4.bias': 0.041362613439559937, 'grad_norm/fc5.weight': 0.7535152435302734, 'grad_norm/fc5.bias': 0.07246360927820206}\n",
-      "Testing complete. Loss: 0.6699, Accuracy: 78.28%\n"
+      "Epoch [1/5] Training complete. Loss: 0.8909, Accuracy: 68.81%\n",
+      "{'layers/layer_fc1/activation_mean': -28.729677200317383, 'layers/layer_fc4/activation_mean': -28.729677200317383, 'layers/layer_fc5/activation_mean': -28.729677200317383, 'layers/layer_fc2/activation_mean': -2.5397043228149414, 'layers/layer_fc3/activation_mean': -3.0052473545074463, 'layers/layer_fc1/activation_std': 10.17672348022461, 'layers/layer_fc4/activation_std': 10.17672348022461, 'layers/layer_fc5/activation_std': 10.17672348022461, 'layers/layer_fc2/activation_std': 2.3032867908477783, 'layers/layer_fc3/activation_std': 3.2448129653930664, 'layers/layer_fc1.weight_mean': -5.6409244280075654e-05, 'layers/layer_fc1.bias_mean': 8.269915269920602e-05, 'layers/layer_fc2.weight_mean': -2.23042461584555e-05, 'layers/layer_fc2.bias_mean': -4.0248556615551934e-05, 'layers/layer_fc3.weight_mean': -1.6235278962994926e-05, 'layers/layer_fc3.bias_mean': -8.13562364783138e-05, 'layers/layer_fc4.weight_mean': -7.563584222225472e-05, 'layers/layer_fc4.bias_mean': -0.00028862591716460884, 'layers/layer_fc5.weight_mean': 1.3050055480690048e-09, 'layers/layer_fc5.bias_mean': 1.862645149230957e-09, 'layers/layer_fc1.weight_std': 0.0022773402743041515, 'layers/layer_fc1.bias_std': 0.0024427783209830523, 'layers/layer_fc2.weight_std': 0.0020020941738039255, 'layers/layer_fc2.bias_std': 0.0015959893353283405, 'layers/layer_fc3.weight_std': 0.0006584679940715432, 'layers/layer_fc3.bias_std': 0.0010985822882503271, 'layers/layer_fc4.weight_std': 0.0016911266138777137, 'layers/layer_fc4.bias_std': 0.0016698684776201844, 'layers/layer_fc5.weight_std': 0.016184095293283463, 'layers/layer_fc5.bias_std': 0.014144722372293472, 'grad_norm/fc1.weight': 1.020559310913086, 'grad_norm/fc1.bias': 0.03903047367930412, 'grad_norm/fc2.weight': 0.7248778939247131, 'grad_norm/fc2.bias': 0.036089323461055756, 'grad_norm/fc3.weight': 0.2384623885154724, 'grad_norm/fc3.bias': 0.01759118027985096, 'grad_norm/fc4.weight': 0.3064279556274414, 'grad_norm/fc4.bias': 0.019099673256278038, 'grad_norm/fc5.weight': 0.5787935256958008, 'grad_norm/fc5.bias': 0.042434170842170715}\n",
+      "Epoch [2/5] Training complete. Loss: 0.2732, Accuracy: 91.75%\n",
+      "{'layers/layer_fc1/activation_mean': -28.754409790039062, 'layers/layer_fc4/activation_mean': -28.754409790039062, 'layers/layer_fc5/activation_mean': -28.754409790039062, 'layers/layer_fc2/activation_mean': -3.4414615631103516, 'layers/layer_fc3/activation_mean': -3.730855703353882, 'layers/layer_fc1/activation_std': 10.266525268554688, 'layers/layer_fc4/activation_std': 10.266525268554688, 'layers/layer_fc5/activation_std': 10.266525268554688, 'layers/layer_fc2/activation_std': 2.995718479156494, 'layers/layer_fc3/activation_std': 3.8177525997161865, 'layers/layer_fc1.weight_mean': 7.965337863424793e-05, 'layers/layer_fc1.bias_mean': -9.780008986126631e-05, 'layers/layer_fc2.weight_mean': -1.2387715287331957e-05, 'layers/layer_fc2.bias_mean': 2.7172100089956075e-06, 'layers/layer_fc3.weight_mean': -1.2878872439614497e-05, 'layers/layer_fc3.bias_mean': -8.313333091791719e-05, 'layers/layer_fc4.weight_mean': -2.042674896074459e-05, 'layers/layer_fc4.bias_mean': -0.0001337243738817051, 'layers/layer_fc5.weight_mean': 1.2068994414971002e-10, 'layers/layer_fc5.bias_mean': -3.2596289556430236e-10, 'layers/layer_fc1.weight_std': 0.0020513804629445076, 'layers/layer_fc1.bias_std': 0.0022429414093494415, 'layers/layer_fc2.weight_std': 0.0027040187269449234, 'layers/layer_fc2.bias_std': 0.0019432309782132506, 'layers/layer_fc3.weight_std': 0.0007950548897497356, 'layers/layer_fc3.bias_std': 0.0013046229723840952, 'layers/layer_fc4.weight_std': 0.002085754182189703, 'layers/layer_fc4.bias_std': 0.0016170400194823742, 'layers/layer_fc5.weight_std': 0.022946881130337715, 'layers/layer_fc5.bias_std': 0.01414299663156271, 'grad_norm/fc1.weight': 0.9197079539299011, 'grad_norm/fc1.bias': 0.035851072520017624, 'grad_norm/fc2.weight': 0.9789657592773438, 'grad_norm/fc2.bias': 0.043927378952503204, 'grad_norm/fc3.weight': 0.2878772020339966, 'grad_norm/fc3.bias': 0.02087557688355446, 'grad_norm/fc4.weight': 0.37757423520088196, 'grad_norm/fc4.bias': 0.018285810947418213, 'grad_norm/fc5.weight': 0.8206518888473511, 'grad_norm/fc5.bias': 0.04242899268865585}\n",
+      "Epoch [3/5] Training complete. Loss: 0.2083, Accuracy: 93.61%\n",
+      "{'layers/layer_fc1/activation_mean': -28.73628807067871, 'layers/layer_fc4/activation_mean': -28.73628807067871, 'layers/layer_fc5/activation_mean': -28.73628807067871, 'layers/layer_fc2/activation_mean': -4.065245151519775, 'layers/layer_fc3/activation_mean': -5.453749656677246, 'layers/layer_fc1/activation_std': 10.463916778564453, 'layers/layer_fc4/activation_std': 10.463916778564453, 'layers/layer_fc5/activation_std': 10.463916778564453, 'layers/layer_fc2/activation_std': 3.5451228618621826, 'layers/layer_fc3/activation_std': 5.318454742431641, 'layers/layer_fc1.weight_mean': 9.08636320673395e-06, 'layers/layer_fc1.bias_mean': -1.3908885193814058e-05, 'layers/layer_fc2.weight_mean': -2.5884002752718516e-05, 'layers/layer_fc2.bias_mean': -8.652423275634646e-05, 'layers/layer_fc3.weight_mean': -1.641714152356144e-05, 'layers/layer_fc3.bias_mean': -8.157885167747736e-05, 'layers/layer_fc4.weight_mean': -3.9947924960870296e-05, 'layers/layer_fc4.bias_mean': -0.00012426286411937326, 'layers/layer_fc5.weight_mean': 3.299284456748097e-10, 'layers/layer_fc5.bias_mean': 1.8626451769865326e-10, 'layers/layer_fc1.weight_std': 0.0009469661745242774, 'layers/layer_fc1.bias_std': 0.0010165267158299685, 'layers/layer_fc2.weight_std': 0.0015304741682484746, 'layers/layer_fc2.bias_std': 0.0007763532339595258, 'layers/layer_fc3.weight_std': 0.000502292939927429, 'layers/layer_fc3.bias_std': 0.0006498315487988293, 'layers/layer_fc4.weight_std': 0.0015387408202514052, 'layers/layer_fc4.bias_std': 0.0010592670878395438, 'layers/layer_fc5.weight_std': 0.016189604997634888, 'layers/layer_fc5.bias_std': 0.010295048356056213, 'grad_norm/fc1.weight': 0.4242599606513977, 'grad_norm/fc1.bias': 0.016234157606959343, 'grad_norm/fc2.weight': 0.5541679263114929, 'grad_norm/fc2.bias': 0.017658572643995285, 'grad_norm/fc3.weight': 0.18194584548473358, 'grad_norm/fc3.bias': 0.010458745993673801, 'grad_norm/fc4.weight': 0.27863141894340515, 'grad_norm/fc4.bias': 0.012019835412502289, 'grad_norm/fc5.weight': 0.5789905786514282, 'grad_norm/fc5.bias': 0.03088514506816864}\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[73], line 47\u001b[0m\n\u001b[0;32m     43\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m correct_preds \u001b[38;5;241m/\u001b[39m total_preds\n\u001b[0;32m     44\u001b[0m \u001b[38;5;66;03m# print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     45\u001b[0m         \n\u001b[0;32m     46\u001b[0m \u001b[38;5;66;03m# Validation step per training step\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m val_loss, val_accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Evaluate after each step\u001b[39;00m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;66;03m# print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     50\u001b[0m run\u001b[38;5;241m.\u001b[39mlog_metrics(\n\u001b[0;32m     51\u001b[0m     data \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     52\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetrics/train/loss\u001b[39m\u001b[38;5;124m\"\u001b[39m: loss\u001b[38;5;241m.\u001b[39mitem(),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     58\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_counter\n\u001b[0;32m     59\u001b[0m )\n",
+      "Cell \u001b[1;32mIn[46], line 8\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, data_loader, track_gradients)\u001b[0m\n\u001b[0;32m      6\u001b[0m epoch_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():  \u001b[38;5;66;03m# Disable gradient tracking during evaluation\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# Forward pass (with gradient tracking if specified)\u001b[39;49;00m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Correct loss computation\u001b[39;49;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\datasets\\mnist.py:146\u001b[0m, in \u001b[0;36mMNIST.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    143\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img\u001b[38;5;241m.\u001b[39mnumpy(), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 146\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    149\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:277\u001b[0m, in \u001b[0;36mNormalize.forward\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, tensor: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    270\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    271\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    272\u001b[0m \u001b[38;5;124;03m        tensor (Tensor): Tensor image to be normalized.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;124;03m        Tensor: Normalized Tensor image.\u001b[39;00m\n\u001b[0;32m    276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\functional.py:350\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensor, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg should be Tensor Image. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tensor)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\_functional_tensor.py:920\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    917\u001b[0m     tensor \u001b[38;5;241m=\u001b[39m tensor\u001b[38;5;241m.\u001b[39mclone()\n\u001b[0;32m    919\u001b[0m dtype \u001b[38;5;241m=\u001b[39m tensor\u001b[38;5;241m.\u001b[39mdtype\n\u001b[1;32m--> 920\u001b[0m mean \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    921\u001b[0m std \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(std, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m    922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (std \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39many():\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -377,7 +398,7 @@
     "        )\n",
     "    \n",
     "    # Print loss and accuracy for the entire training epoch\n",
-    "    train_accuracy = 100 * correct_preds / total_preds\n",
+    "    train_accuracy = correct_preds / total_preds\n",
     "    print(f\"Epoch [{epoch+1}/{num_epochs}] Training complete. Loss: {epoch_loss / len(train_loader):.4f}, Accuracy: {train_accuracy:.2f}%\")\n",
     "\n",
     "    # Track activations\n",
@@ -424,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {

From 3e2d453a4a61ddfcbc3eda2fe9af17b4d74d8c53 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 28 Feb 2025 08:32:59 +0100
Subject: [PATCH 012/125] refactor: update model architecture layers, accuracy
 calculation and calculate gradient norms for batch (step) rather than epoch

---
 .../pytorch_image_classification.ipynb        | 51 ++++++++++---------
 1 file changed, 28 insertions(+), 23 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 69f68c7..c36f7bd 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -57,7 +57,10 @@
     "# TODO - look at CNN layers\n",
     "# TODO - output and log the model architecture\n",
     "# TODO - check loss and accuracy calculations\n",
-    "# TODO - clean up the evaluation function to exclude tracking gradients"
+    "# TODO - clean up the evaluation function to exclude tracking gradients\n",
+    "# TODO - do not use group tags\n",
+    "# TODO - track the input features\n",
+    "# TODO - clean the training loop of commented out code that is unused"
    ]
   },
   {
@@ -84,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -223,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,19 +301,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch [1/5] Training complete. Loss: 0.8909, Accuracy: 68.81%\n",
-      "{'layers/layer_fc1/activation_mean': -28.729677200317383, 'layers/layer_fc4/activation_mean': -28.729677200317383, 'layers/layer_fc5/activation_mean': -28.729677200317383, 'layers/layer_fc2/activation_mean': -2.5397043228149414, 'layers/layer_fc3/activation_mean': -3.0052473545074463, 'layers/layer_fc1/activation_std': 10.17672348022461, 'layers/layer_fc4/activation_std': 10.17672348022461, 'layers/layer_fc5/activation_std': 10.17672348022461, 'layers/layer_fc2/activation_std': 2.3032867908477783, 'layers/layer_fc3/activation_std': 3.2448129653930664, 'layers/layer_fc1.weight_mean': -5.6409244280075654e-05, 'layers/layer_fc1.bias_mean': 8.269915269920602e-05, 'layers/layer_fc2.weight_mean': -2.23042461584555e-05, 'layers/layer_fc2.bias_mean': -4.0248556615551934e-05, 'layers/layer_fc3.weight_mean': -1.6235278962994926e-05, 'layers/layer_fc3.bias_mean': -8.13562364783138e-05, 'layers/layer_fc4.weight_mean': -7.563584222225472e-05, 'layers/layer_fc4.bias_mean': -0.00028862591716460884, 'layers/layer_fc5.weight_mean': 1.3050055480690048e-09, 'layers/layer_fc5.bias_mean': 1.862645149230957e-09, 'layers/layer_fc1.weight_std': 0.0022773402743041515, 'layers/layer_fc1.bias_std': 0.0024427783209830523, 'layers/layer_fc2.weight_std': 0.0020020941738039255, 'layers/layer_fc2.bias_std': 0.0015959893353283405, 'layers/layer_fc3.weight_std': 0.0006584679940715432, 'layers/layer_fc3.bias_std': 0.0010985822882503271, 'layers/layer_fc4.weight_std': 0.0016911266138777137, 'layers/layer_fc4.bias_std': 0.0016698684776201844, 'layers/layer_fc5.weight_std': 0.016184095293283463, 'layers/layer_fc5.bias_std': 0.014144722372293472, 'grad_norm/fc1.weight': 1.020559310913086, 'grad_norm/fc1.bias': 0.03903047367930412, 'grad_norm/fc2.weight': 0.7248778939247131, 'grad_norm/fc2.bias': 0.036089323461055756, 'grad_norm/fc3.weight': 0.2384623885154724, 'grad_norm/fc3.bias': 0.01759118027985096, 'grad_norm/fc4.weight': 0.3064279556274414, 'grad_norm/fc4.bias': 0.019099673256278038, 'grad_norm/fc5.weight': 0.5787935256958008, 'grad_norm/fc5.bias': 0.042434170842170715}\n",
-      "Epoch [2/5] Training complete. Loss: 0.2732, Accuracy: 91.75%\n",
-      "{'layers/layer_fc1/activation_mean': -28.754409790039062, 'layers/layer_fc4/activation_mean': -28.754409790039062, 'layers/layer_fc5/activation_mean': -28.754409790039062, 'layers/layer_fc2/activation_mean': -3.4414615631103516, 'layers/layer_fc3/activation_mean': -3.730855703353882, 'layers/layer_fc1/activation_std': 10.266525268554688, 'layers/layer_fc4/activation_std': 10.266525268554688, 'layers/layer_fc5/activation_std': 10.266525268554688, 'layers/layer_fc2/activation_std': 2.995718479156494, 'layers/layer_fc3/activation_std': 3.8177525997161865, 'layers/layer_fc1.weight_mean': 7.965337863424793e-05, 'layers/layer_fc1.bias_mean': -9.780008986126631e-05, 'layers/layer_fc2.weight_mean': -1.2387715287331957e-05, 'layers/layer_fc2.bias_mean': 2.7172100089956075e-06, 'layers/layer_fc3.weight_mean': -1.2878872439614497e-05, 'layers/layer_fc3.bias_mean': -8.313333091791719e-05, 'layers/layer_fc4.weight_mean': -2.042674896074459e-05, 'layers/layer_fc4.bias_mean': -0.0001337243738817051, 'layers/layer_fc5.weight_mean': 1.2068994414971002e-10, 'layers/layer_fc5.bias_mean': -3.2596289556430236e-10, 'layers/layer_fc1.weight_std': 0.0020513804629445076, 'layers/layer_fc1.bias_std': 0.0022429414093494415, 'layers/layer_fc2.weight_std': 0.0027040187269449234, 'layers/layer_fc2.bias_std': 0.0019432309782132506, 'layers/layer_fc3.weight_std': 0.0007950548897497356, 'layers/layer_fc3.bias_std': 0.0013046229723840952, 'layers/layer_fc4.weight_std': 0.002085754182189703, 'layers/layer_fc4.bias_std': 0.0016170400194823742, 'layers/layer_fc5.weight_std': 0.022946881130337715, 'layers/layer_fc5.bias_std': 0.01414299663156271, 'grad_norm/fc1.weight': 0.9197079539299011, 'grad_norm/fc1.bias': 0.035851072520017624, 'grad_norm/fc2.weight': 0.9789657592773438, 'grad_norm/fc2.bias': 0.043927378952503204, 'grad_norm/fc3.weight': 0.2878772020339966, 'grad_norm/fc3.bias': 0.02087557688355446, 'grad_norm/fc4.weight': 0.37757423520088196, 'grad_norm/fc4.bias': 0.018285810947418213, 'grad_norm/fc5.weight': 0.8206518888473511, 'grad_norm/fc5.bias': 0.04242899268865585}\n",
-      "Epoch [3/5] Training complete. Loss: 0.2083, Accuracy: 93.61%\n",
-      "{'layers/layer_fc1/activation_mean': -28.73628807067871, 'layers/layer_fc4/activation_mean': -28.73628807067871, 'layers/layer_fc5/activation_mean': -28.73628807067871, 'layers/layer_fc2/activation_mean': -4.065245151519775, 'layers/layer_fc3/activation_mean': -5.453749656677246, 'layers/layer_fc1/activation_std': 10.463916778564453, 'layers/layer_fc4/activation_std': 10.463916778564453, 'layers/layer_fc5/activation_std': 10.463916778564453, 'layers/layer_fc2/activation_std': 3.5451228618621826, 'layers/layer_fc3/activation_std': 5.318454742431641, 'layers/layer_fc1.weight_mean': 9.08636320673395e-06, 'layers/layer_fc1.bias_mean': -1.3908885193814058e-05, 'layers/layer_fc2.weight_mean': -2.5884002752718516e-05, 'layers/layer_fc2.bias_mean': -8.652423275634646e-05, 'layers/layer_fc3.weight_mean': -1.641714152356144e-05, 'layers/layer_fc3.bias_mean': -8.157885167747736e-05, 'layers/layer_fc4.weight_mean': -3.9947924960870296e-05, 'layers/layer_fc4.bias_mean': -0.00012426286411937326, 'layers/layer_fc5.weight_mean': 3.299284456748097e-10, 'layers/layer_fc5.bias_mean': 1.8626451769865326e-10, 'layers/layer_fc1.weight_std': 0.0009469661745242774, 'layers/layer_fc1.bias_std': 0.0010165267158299685, 'layers/layer_fc2.weight_std': 0.0015304741682484746, 'layers/layer_fc2.bias_std': 0.0007763532339595258, 'layers/layer_fc3.weight_std': 0.000502292939927429, 'layers/layer_fc3.bias_std': 0.0006498315487988293, 'layers/layer_fc4.weight_std': 0.0015387408202514052, 'layers/layer_fc4.bias_std': 0.0010592670878395438, 'layers/layer_fc5.weight_std': 0.016189604997634888, 'layers/layer_fc5.bias_std': 0.010295048356056213, 'grad_norm/fc1.weight': 0.4242599606513977, 'grad_norm/fc1.bias': 0.016234157606959343, 'grad_norm/fc2.weight': 0.5541679263114929, 'grad_norm/fc2.bias': 0.017658572643995285, 'grad_norm/fc3.weight': 0.18194584548473358, 'grad_norm/fc3.bias': 0.010458745993673801, 'grad_norm/fc4.weight': 0.27863141894340515, 'grad_norm/fc4.bias': 0.012019835412502289, 'grad_norm/fc5.weight': 0.5789905786514282, 'grad_norm/fc5.bias': 0.03088514506816864}\n"
+      "Epoch [1/5] Training complete. Loss: 0.9066, Accuracy: 0.69%\n",
+      "{'layers/layer_fc1/activation_mean': -27.44853973388672, 'layers/layer_fc4/activation_mean': -27.44853973388672, 'layers/layer_fc5/activation_mean': -27.44853973388672, 'layers/layer_fc2/activation_mean': -3.266420602798462, 'layers/layer_fc3/activation_mean': -3.977348804473877, 'layers/layer_fc1/activation_std': 9.884493827819824, 'layers/layer_fc4/activation_std': 9.884493827819824, 'layers/layer_fc5/activation_std': 9.884493827819824, 'layers/layer_fc2/activation_std': 2.8974952697753906, 'layers/layer_fc3/activation_std': 3.9147353172302246, 'layers/layer_fc1.weight_mean': 1.759609585860744e-05, 'layers/layer_fc1.bias_mean': -2.6617166440701112e-05, 'layers/layer_fc2.weight_mean': 1.845465158112347e-05, 'layers/layer_fc2.bias_mean': 6.048093564459123e-05, 'layers/layer_fc3.weight_mean': 6.554154424520675e-06, 'layers/layer_fc3.bias_mean': 4.6133769501466304e-05, 'layers/layer_fc4.weight_mean': -1.4562309843313415e-05, 'layers/layer_fc4.bias_mean': 1.2838396287406795e-05, 'layers/layer_fc5.weight_mean': 4.2628095497931895e-10, 'layers/layer_fc5.bias_mean': -9.313225884932663e-11, 'layers/layer_fc1.weight_std': 0.0014361083740368485, 'layers/layer_fc1.bias_std': 0.0014619147405028343, 'layers/layer_fc2.weight_std': 0.001974125625565648, 'layers/layer_fc2.bias_std': 0.0011026636930182576, 'layers/layer_fc3.weight_std': 0.0008759713382460177, 'layers/layer_fc3.bias_std': 0.0011844916734844446, 'layers/layer_fc4.weight_std': 0.002118356991559267, 'layers/layer_fc4.bias_std': 0.00178423966281116, 'layers/layer_fc5.weight_std': 0.021144278347492218, 'layers/layer_fc5.bias_std': 0.016225792467594147, 'grad_norm/fc1.weight': 0.6434243321418762, 'grad_norm/fc1.bias': 0.023348789662122726, 'grad_norm/fc2.weight': 0.714738130569458, 'grad_norm/fc2.bias': 0.02496359497308731, 'grad_norm/fc3.weight': 0.31714311242103577, 'grad_norm/fc3.bias': 0.018929213285446167, 'grad_norm/fc4.weight': 0.38346678018569946, 'grad_norm/fc4.bias': 0.02010788396000862, 'grad_norm/fc5.weight': 0.7561851143836975, 'grad_norm/fc5.bias': 0.04867737740278244}\n",
+      "Epoch [2/5] Training complete. Loss: 0.2738, Accuracy: 0.92%\n",
+      "{'layers/layer_fc1/activation_mean': -27.444847106933594, 'layers/layer_fc4/activation_mean': -27.444847106933594, 'layers/layer_fc5/activation_mean': -27.444847106933594, 'layers/layer_fc2/activation_mean': -4.043460845947266, 'layers/layer_fc3/activation_mean': -4.557342529296875, 'layers/layer_fc1/activation_std': 9.927286148071289, 'layers/layer_fc4/activation_std': 9.927286148071289, 'layers/layer_fc5/activation_std': 9.927286148071289, 'layers/layer_fc2/activation_std': 3.500694990158081, 'layers/layer_fc3/activation_std': 4.5128021240234375, 'layers/layer_fc1.weight_mean': 3.658090008684667e-07, 'layers/layer_fc1.bias_mean': 3.3925662137335166e-06, 'layers/layer_fc2.weight_mean': -2.423007288143708e-07, 'layers/layer_fc2.bias_mean': 1.9860681277350523e-05, 'layers/layer_fc3.weight_mean': 5.834312105434947e-06, 'layers/layer_fc3.bias_mean': -6.391452188836411e-06, 'layers/layer_fc4.weight_mean': 7.309272405109368e-06, 'layers/layer_fc4.bias_mean': 3.118724634987302e-06, 'layers/layer_fc5.weight_mean': 3.8910458188823327e-10, 'layers/layer_fc5.bias_mean': 1.3038515822572094e-09, 'layers/layer_fc1.weight_std': 0.0011528775794431567, 'layers/layer_fc1.bias_std': 0.0012104393681511283, 'layers/layer_fc2.weight_std': 0.0022255387157201767, 'layers/layer_fc2.bias_std': 0.0010018462780863047, 'layers/layer_fc3.weight_std': 0.0007322281016968191, 'layers/layer_fc3.bias_std': 0.0008695347351022065, 'layers/layer_fc4.weight_std': 0.0019773344974964857, 'layers/layer_fc4.bias_std': 0.0012858008267357945, 'layers/layer_fc5.weight_std': 0.015919363126158714, 'layers/layer_fc5.bias_std': 0.011463840492069721, 'grad_norm/fc1.weight': 0.5164875984191895, 'grad_norm/fc1.bias': 0.019329242408275604, 'grad_norm/fc2.weight': 0.8057278990745544, 'grad_norm/fc2.bias': 0.02265150472521782, 'grad_norm/fc3.weight': 0.26510223746299744, 'grad_norm/fc3.bias': 0.013885731808841228, 'grad_norm/fc4.weight': 0.3579327464103699, 'grad_norm/fc4.bias': 0.014490283094346523, 'grad_norm/fc5.weight': 0.5693259239196777, 'grad_norm/fc5.bias': 0.03439152240753174}\n",
+      "Epoch [3/5] Training complete. Loss: 0.2052, Accuracy: 0.94%\n",
+      "{'layers/layer_fc1/activation_mean': -27.409822463989258, 'layers/layer_fc4/activation_mean': -27.409822463989258, 'layers/layer_fc5/activation_mean': -27.409822463989258, 'layers/layer_fc2/activation_mean': -4.827888011932373, 'layers/layer_fc3/activation_mean': -5.646310329437256, 'layers/layer_fc1/activation_std': 10.087625503540039, 'layers/layer_fc4/activation_std': 10.087625503540039, 'layers/layer_fc5/activation_std': 10.087625503540039, 'layers/layer_fc2/activation_std': 4.256272792816162, 'layers/layer_fc3/activation_std': 5.8036675453186035, 'layers/layer_fc1.weight_mean': -1.7095830116886646e-05, 'layers/layer_fc1.bias_mean': 2.0519555619102903e-05, 'layers/layer_fc2.weight_mean': -1.0371921234764159e-05, 'layers/layer_fc2.bias_mean': -2.6764431822812185e-05, 'layers/layer_fc3.weight_mean': 1.1725012427632464e-07, 'layers/layer_fc3.bias_mean': -3.2187872420763597e-06, 'layers/layer_fc4.weight_mean': -4.269544206181308e-06, 'layers/layer_fc4.bias_mean': 2.4562015823903494e-05, 'layers/layer_fc5.weight_mean': 5.096634225765229e-10, 'layers/layer_fc5.bias_mean': 1.3038515822572094e-09, 'layers/layer_fc1.weight_std': 0.0010182075202465057, 'layers/layer_fc1.bias_std': 0.0011107329046353698, 'layers/layer_fc2.weight_std': 0.0014369278214871883, 'layers/layer_fc2.bias_std': 0.0006925289635546505, 'layers/layer_fc3.weight_std': 0.0004932511947117746, 'layers/layer_fc3.bias_std': 0.0006599067128263414, 'layers/layer_fc4.weight_std': 0.0011504496214911342, 'layers/layer_fc4.bias_std': 0.001063313102349639, 'layers/layer_fc5.weight_std': 0.010835869237780571, 'layers/layer_fc5.bias_std': 0.010039512068033218, 'grad_norm/fc1.weight': 0.4562200903892517, 'grad_norm/fc1.bias': 0.017740018665790558, 'grad_norm/fc2.weight': 0.520235002040863, 'grad_norm/fc2.bias': 0.01566654071211815, 'grad_norm/fc3.weight': 0.1785753071308136, 'grad_norm/fc3.bias': 0.010537991300225258, 'grad_norm/fc4.weight': 0.20825187861919403, 'grad_norm/fc4.bias': 0.011986151337623596, 'grad_norm/fc5.weight': 0.3875243663787842, 'grad_norm/fc5.bias': 0.030118538066744804}\n"
      ]
     },
     {
@@ -320,18 +323,15 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[73], line 47\u001b[0m\n\u001b[0;32m     43\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m correct_preds \u001b[38;5;241m/\u001b[39m total_preds\n\u001b[0;32m     44\u001b[0m \u001b[38;5;66;03m# print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     45\u001b[0m         \n\u001b[0;32m     46\u001b[0m \u001b[38;5;66;03m# Validation step per training step\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m val_loss, val_accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Evaluate after each step\u001b[39;00m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;66;03m# print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     50\u001b[0m run\u001b[38;5;241m.\u001b[39mlog_metrics(\n\u001b[0;32m     51\u001b[0m     data \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     52\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetrics/train/loss\u001b[39m\u001b[38;5;124m\"\u001b[39m: loss\u001b[38;5;241m.\u001b[39mitem(),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     58\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_counter\n\u001b[0;32m     59\u001b[0m )\n",
-      "Cell \u001b[1;32mIn[46], line 8\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, data_loader, track_gradients)\u001b[0m\n\u001b[0;32m      6\u001b[0m epoch_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():  \u001b[38;5;66;03m# Disable gradient tracking during evaluation\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# Forward pass (with gradient tracking if specified)\u001b[39;49;00m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Correct loss computation\u001b[39;49;00m\n",
+      "Cell \u001b[1;32mIn[94], line 47\u001b[0m\n\u001b[0;32m     43\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m correct_preds \u001b[38;5;241m/\u001b[39m total_preds\n\u001b[0;32m     44\u001b[0m \u001b[38;5;66;03m# print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     45\u001b[0m         \n\u001b[0;32m     46\u001b[0m \u001b[38;5;66;03m# Validation step per training step\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m val_loss, val_accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Evaluate after each step\u001b[39;00m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;66;03m# print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, param \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mnamed_parameters():\n",
+      "Cell \u001b[1;32mIn[80], line 8\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, data_loader, track_gradients)\u001b[0m\n\u001b[0;32m      6\u001b[0m epoch_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():  \u001b[38;5;66;03m# Disable gradient tracking during evaluation\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# Forward pass (with gradient tracking if specified)\u001b[39;49;00m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Correct loss computation\u001b[39;49;00m\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\datasets\\mnist.py:146\u001b[0m, in \u001b[0;36mMNIST.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    143\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img\u001b[38;5;241m.\u001b[39mnumpy(), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 146\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    149\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:277\u001b[0m, in \u001b[0;36mNormalize.forward\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, tensor: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    270\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    271\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    272\u001b[0m \u001b[38;5;124;03m        tensor (Tensor): Tensor image to be normalized.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;124;03m        Tensor: Normalized Tensor image.\u001b[39;00m\n\u001b[0;32m    276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\functional.py:350\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensor, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg should be Tensor Image. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tensor)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\_functional_tensor.py:920\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    917\u001b[0m     tensor \u001b[38;5;241m=\u001b[39m tensor\u001b[38;5;241m.\u001b[39mclone()\n\u001b[0;32m    919\u001b[0m dtype \u001b[38;5;241m=\u001b[39m tensor\u001b[38;5;241m.\u001b[39mdtype\n\u001b[1;32m--> 920\u001b[0m mean \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    921\u001b[0m std \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(std, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m    922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (std \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39many():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:137\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[1;34m(self, pic)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pic):\n\u001b[0;32m    130\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    131\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    132\u001b[0m \u001b[38;5;124;03m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;124;03m        Tensor: Converted image.\u001b[39;00m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpic\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\functional.py:176\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m    174\u001b[0m img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(img, torch\u001b[38;5;241m.\u001b[39mByteTensor):\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_float_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdiv(\u001b[38;5;241m255\u001b[39m)\n\u001b[0;32m    177\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    178\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -379,20 +379,25 @@
     "        \n",
     "        # Print loss and accuracy for each batch (step)\n",
     "        #if (batch_idx + 1) % 5 == 0:  # Every 5 steps\n",
-    "        batch_accuracy = 100 * correct_preds / total_preds\n",
+    "        batch_accuracy = correct_preds / total_preds\n",
     "        # print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\n",
     "                \n",
     "        # Validation step per training step\n",
     "        val_loss, val_accuracy = evaluate(model, val_loader)  # Evaluate after each step\n",
     "        # print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\n",
     "\n",
+    "        for name, param in model.named_parameters():\n",
+    "            if param is not None:\n",
+    "                grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
+    "\n",
     "        run.log_metrics(\n",
     "            data = {\n",
     "                \"metrics/train/loss\": loss.item(),\n",
     "                \"metrics/train/accuracy\": batch_accuracy,\n",
     "                \"metrics/validation/loss\": val_loss,\n",
     "                \"metrics/validation/accuracy\": val_accuracy,\n",
-    "                \"epoch_value\": epoch\n",
+    "                \"epoch_value\": epoch,\n",
+    "                **grad_norms\n",
     "            },\n",
     "            step = step_counter\n",
     "        )\n",
@@ -411,7 +416,7 @@
     "        if param is not None:\n",
     "            params_dict_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
     "            params_dict_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "            grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
+    "            # grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
     "    \n",
     "    layers_dict = {**activation_dict_mean, \n",
     "                   **activation_dict_std,\n",
@@ -445,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {

From 8a3da577769ac9be330234a164c0d7fd95b5424d Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 12:04:58 +0100
Subject: [PATCH 013/125] feat: Added a pytorch text-based example that is used
 to demonstrate debugging when building LLM's

---
 .../pytorch_text_model_debugging.ipynb        | 646 ++++++++++++++++++
 1 file changed, 646 insertions(+)
 create mode 100644 integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
new file mode 100644
index 0000000..4c8f180
--- /dev/null
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -0,0 +1,646 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO - capture logs to Neptune\n",
+    "# TODO - improve dataset to be more realistic\n",
+    "# TODO - capture the folowings; attentions, activations, gradients, weights, loss (agg and per layer), learning rate (per layer if needed)\n",
+    "# TODO - capture per layer losses\n",
+    "# TODO - increase number of layers\n",
+    "# TODO - Adding dropout to demonstrate the effects of regularization\n",
+    "# TODO - batch normalization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During training, capturing valuable information from each layer of a model can provide insights into how the model is learning, what features are being extracted, and how the model's performance can be improved. Researchers often look for certain metrics and activations from each layer, which can help in diagnosing issues such as vanishing gradients, overfitting, or underfitting.\n",
+    "\n",
+    "Key Information to Capture from Each Layer:\n",
+    "Activations:\n",
+    "\n",
+    "What it shows: The activations of each layer during forward propagation can give insights into how the model is processing input data at different levels. By examining activations, you can see if certain layers are \"dead\" (i.e., not learning anything) or if activations are exploding (leading to unstable training).\n",
+    "How to capture: You can register hooks to capture activations after each layer.\n",
+    "Gradients:\n",
+    "\n",
+    "What it shows: The gradients of each layer during backpropagation are important for diagnosing issues like vanishing or exploding gradients. If gradients are too small, it can indicate the model is struggling to learn effectively (vanishing gradients). If they're too large, it can indicate instability (exploding gradients).\n",
+    "How to capture: Similar to activations, you can use hooks to capture gradients during backpropagation.\n",
+    "Weights and Biases:\n",
+    "\n",
+    "What it shows: Tracking the weights and biases of each layer helps researchers understand how the model's parameters are evolving during training. For example, if weights are growing too large or becoming too small, it might suggest the need for better regularization or learning rate adjustments.\n",
+    "How to capture: You can extract weights and biases directly from the model’s parameters.\n",
+    "Layer-wise Loss:\n",
+    "\n",
+    "What it shows: Tracking loss at different stages (layers) can help understand which parts of the network are contributing more to the overall loss. This can be valuable for debugging or optimizing the model.\n",
+    "How to capture: Loss can be tracked by monitoring the output from each layer and comparing it against the target.\n",
+    "Learning Rate per Layer:\n",
+    "\n",
+    "What it shows: If you're using techniques like Layer-wise Learning Rate Decay (L2LRD) or applying different learning rates to different layers, capturing the learning rate used by each layer during training can be informative.\n",
+    "How to capture: This information would depend on how the learning rate is defined in your optimizer, but it can be tracked manually.\n",
+    "Layer Output Norms:\n",
+    "\n",
+    "What it shows: Tracking the L2-norm (magnitude) of the output for each layer can help in detecting issues like gradient explosion or squashing effects. A norm that’s either too high or too low could suggest the model isn't behaving as expected.\n",
+    "How to capture: You can compute the L2-norm for the output of each layer.\n",
+    "Activation Distributions:\n",
+    "\n",
+    "What it shows: Visualizing or capturing the distribution of activations (e.g., mean, variance, or histograms) can reveal issues like the saturation of neurons in activation functions, especially in cases with ReLU (leading to dead neurons).\n",
+    "How to capture: You can visualize or compute statistical summaries of activations using Python libraries like matplotlib or seaborn.\n",
+    "Feature Maps (for Convolutional Layers):\n",
+    "\n",
+    "What it shows: If you have convolutional layers, tracking the feature maps can provide insight into how each filter is detecting specific patterns or features from the input data.\n",
+    "How to capture: You can visualize the feature maps after convolutional layers using libraries like matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/400, Loss: 11.5184\n",
+      "Epoch 2/400, Loss: 11.0458\n",
+      "Epoch 3/400, Loss: 10.6166\n",
+      "Epoch 4/400, Loss: 10.2209\n",
+      "Epoch 5/400, Loss: 9.8457\n",
+      "Epoch 6/400, Loss: 9.4807\n",
+      "Epoch 7/400, Loss: 9.1192\n",
+      "Epoch 8/400, Loss: 8.7570\n",
+      "Epoch 9/400, Loss: 8.3914\n",
+      "Epoch 10/400, Loss: 8.0211\n",
+      "Epoch 11/400, Loss: 7.6454\n",
+      "Epoch 12/400, Loss: 7.2639\n",
+      "Epoch 13/400, Loss: 6.8763\n",
+      "Epoch 14/400, Loss: 6.4826\n",
+      "Epoch 15/400, Loss: 6.0829\n",
+      "Epoch 16/400, Loss: 5.6777\n",
+      "Epoch 17/400, Loss: 5.2678\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[7], line 66\u001b[0m\n\u001b[0;32m     63\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(output, target_data)\n\u001b[0;32m     65\u001b[0m \u001b[38;5;66;03m# Backward pass\u001b[39;00m\n\u001b[1;32m---> 66\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[0;32m     69\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the simple LLM model with LSTM\n",
+    "class SimpleLLM(nn.Module):\n",
+    "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
+    "        super(SimpleLLM, self).__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
+    "        self.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)\n",
+    "        self.fc = nn.Linear(hidden_size, vocab_size)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.embedding(x)\n",
+    "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
+    "        out = self.fc(lstm_out)\n",
+    "        return out\n",
+    "\n",
+    "# Define a function to capture activations\n",
+    "def capture_activation(name):\n",
+    "    def hook(model, input, output):\n",
+    "        activations.append((name, output))  # Capture activation\n",
+    "    return hook\n",
+    "\n",
+    "# Define a function to capture gradients\n",
+    "def capture_gradient(name):\n",
+    "    def hook(module, grad_input, grad_output):\n",
+    "        gradients.append((name, grad_output))  # Capture gradients\n",
+    "    return hook\n",
+    "\n",
+    "# Initialize model, loss function, and optimizer\n",
+    "vocab_size = 100000  # Just an example vocab size\n",
+    "embed_size = 1000  # Embedding size\n",
+    "hidden_size = 256  # Hidden size for the LSTM\n",
+    "model = SimpleLLM(vocab_size, embed_size, hidden_size)\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Container for capturing activations and gradients\n",
+    "activations = []\n",
+    "gradients = []\n",
+    "\n",
+    "# Register hooks for LSTM and final Linear layer\n",
+    "model.lstm.register_forward_hook(capture_activation('lstm'))\n",
+    "model.fc.register_forward_hook(capture_activation('fc'))\n",
+    "\n",
+    "model.lstm.register_full_backward_hook(capture_gradient('lstm'))\n",
+    "model.fc.register_full_backward_hook(capture_gradient('fc'))\n",
+    "\n",
+    "# Dummy input data (example with batch_size=3 and sequence_length=5)\n",
+    "input_data = torch.randint(0, vocab_size, (5, 10))  # Random word indices\n",
+    "target_data = torch.randint(0, vocab_size, (5, 10))  # Random target labels\n",
+    "\n",
+    "# Training loop\n",
+    "num_epochs = 400\n",
+    "for epoch in range(num_epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    # Forward pass\n",
+    "    output = model(input_data)\n",
+    "    \n",
+    "    # Reshape output and target to match the shape of CrossEntropyLoss expectations\n",
+    "    output = output.view(-1, vocab_size)  # Flatten the output\n",
+    "    target_data = target_data.view(-1)    # Flatten the target data\n",
+    "    \n",
+    "    # Calculate loss\n",
+    "    loss = criterion(output, target_data)\n",
+    "    \n",
+    "    # Backward pass\n",
+    "    loss.backward()\n",
+    "\n",
+    "    # Optimizer step\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    # Output loss for this epoch\n",
+    "    # if epoch % 50 == 0:\n",
+    "    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Activation for lstm layer:  (tensor([[[-1.1885e-01,  7.4334e-02, -1.1457e-01,  ...,  6.1157e-02,\n",
+      "           3.4403e-02,  1.4354e-01],\n",
+      "         [ 2.1115e-02, -9.9912e-02, -1.5829e-01,  ...,  6.8039e-02,\n",
+      "           5.0781e-02,  1.2131e-01],\n",
+      "         [-5.1509e-02, -2.8223e-02, -1.2977e-01,  ...,  5.5352e-02,\n",
+      "           1.3167e-01, -9.2887e-02],\n",
+      "         [ 1.4676e-01, -5.5235e-02, -2.3041e-01,  ...,  1.3869e-01,\n",
+      "           6.1838e-02, -1.1159e-01],\n",
+      "         [ 9.8354e-03,  5.3392e-02, -3.0002e-02,  ...,  1.5939e-01,\n",
+      "           4.8001e-02, -1.6666e-01]],\n",
+      "\n",
+      "        [[-6.9048e-03, -1.4166e-01,  1.8845e-03,  ...,  1.3928e-01,\n",
+      "          -2.7910e-01,  1.3777e-01],\n",
+      "         [-7.3346e-02, -1.0498e-01,  3.9164e-02,  ...,  1.7106e-01,\n",
+      "          -8.2462e-02, -5.4011e-02],\n",
+      "         [ 1.3681e-01, -5.4486e-02, -7.6502e-02,  ...,  1.2276e-01,\n",
+      "           5.4242e-03, -1.5655e-01],\n",
+      "         [ 1.3117e-01, -1.4139e-01, -2.0100e-01,  ...,  1.5732e-01,\n",
+      "          -1.0666e-01, -8.7156e-02],\n",
+      "         [ 9.4591e-02, -5.7793e-02, -1.3404e-01,  ...,  9.6900e-02,\n",
+      "           4.9515e-03, -7.9186e-03]],\n",
+      "\n",
+      "        [[-9.0859e-02,  5.5639e-03, -1.4513e-01,  ..., -7.1660e-02,\n",
+      "           1.8301e-01,  7.4892e-03],\n",
+      "         [ 9.2305e-02,  1.1849e-01, -1.5432e-01,  ..., -3.9754e-02,\n",
+      "           2.1528e-01, -6.6336e-02],\n",
+      "         [-4.3912e-02,  7.2306e-03, -1.6964e-01,  ...,  3.7397e-02,\n",
+      "           1.3741e-01, -7.8224e-03],\n",
+      "         [-2.3771e-03, -1.7121e-01, -1.9225e-01,  ...,  2.1824e-02,\n",
+      "           3.4948e-02, -1.7641e-04],\n",
+      "         [ 1.3603e-01, -6.7352e-02, -2.7966e-01,  ...,  1.0816e-01,\n",
+      "          -9.4453e-02,  1.8542e-01]]], grad_fn=<TransposeBackward0>), (tensor([[[ 9.8354e-03,  5.3392e-02, -3.0002e-02,  7.2875e-03, -1.4585e-01,\n",
+      "           1.2482e-02,  6.9810e-02, -9.4523e-02,  8.2014e-02,  1.4978e-02,\n",
+      "          -2.2127e-01, -8.6931e-02, -1.7447e-02, -9.0253e-02, -5.8715e-02,\n",
+      "          -9.7126e-02, -1.4640e-02,  6.9750e-02, -4.0392e-02, -6.4946e-03,\n",
+      "           6.3030e-02, -2.8948e-03,  2.3277e-02,  2.1406e-01, -7.2021e-02,\n",
+      "          -8.5262e-02,  2.1904e-01,  5.9262e-02,  8.1430e-02, -5.0410e-02,\n",
+      "           4.7898e-02, -1.7856e-02, -9.6553e-02,  1.2176e-01,  1.3534e-01,\n",
+      "           9.9480e-02, -6.4945e-02, -8.7489e-02,  1.9893e-01, -1.1475e-02,\n",
+      "          -1.1274e-01,  1.4311e-01,  1.2569e-01,  9.3439e-03,  7.4138e-02,\n",
+      "          -4.6937e-02,  1.2031e-01,  3.7596e-02, -1.1246e-01, -1.1382e-01,\n",
+      "          -4.6571e-02, -6.7600e-02, -1.0755e-01,  1.3048e-01, -1.8727e-01,\n",
+      "          -1.5687e-01,  1.3892e-01, -2.1552e-01, -5.3767e-02,  1.5782e-02,\n",
+      "           1.7370e-01,  3.1182e-02,  1.0335e-01,  2.5303e-02, -1.8224e-02,\n",
+      "          -6.4270e-03, -1.8351e-02, -7.7923e-02,  7.3565e-03,  1.0760e-01,\n",
+      "           2.1875e-02, -4.9347e-02,  7.0013e-02, -4.0485e-02,  8.5161e-03,\n",
+      "          -1.6069e-01, -7.3729e-02, -1.1683e-01,  1.1353e-01,  9.2289e-02,\n",
+      "          -2.1123e-01, -2.1108e-02, -3.3057e-02,  9.3739e-02, -7.4621e-02,\n",
+      "          -3.0965e-02,  3.7901e-02,  9.5562e-02,  5.1191e-02, -8.1342e-02,\n",
+      "           1.9611e-02, -1.1581e-01, -2.1830e-01,  1.9827e-02, -3.9519e-02,\n",
+      "           1.1952e-01,  6.1484e-02,  1.3787e-01,  4.1152e-02, -6.1684e-02,\n",
+      "          -5.6594e-02, -3.1420e-02, -2.2474e-02, -6.5657e-02,  1.4875e-01,\n",
+      "          -9.1052e-02,  7.7182e-02,  4.8757e-02,  1.4903e-01, -1.3527e-03,\n",
+      "           2.7246e-01,  1.2794e-01, -1.7041e-01, -7.6142e-02,  1.3974e-01,\n",
+      "           2.4618e-02, -6.9025e-02, -9.4079e-02, -1.4909e-03,  5.9612e-02,\n",
+      "          -4.4725e-03, -6.7179e-02, -6.1797e-02,  1.6559e-01, -8.0163e-02,\n",
+      "          -2.2909e-01,  6.6350e-02,  1.1361e-02,  8.4108e-02, -1.1556e-01,\n",
+      "           6.1252e-02,  8.3287e-02, -8.7894e-02, -1.0695e-01, -5.3925e-02,\n",
+      "           7.1153e-02,  6.9016e-02, -1.4300e-02, -5.9164e-02,  3.7659e-02,\n",
+      "           1.0063e-01,  1.0336e-01, -1.9477e-02,  3.5742e-02, -3.3210e-02,\n",
+      "          -1.6847e-03, -9.3591e-02,  1.4821e-01,  8.3384e-02,  8.9724e-02,\n",
+      "          -1.2488e-01, -1.8335e-01, -1.4115e-01,  1.3415e-01,  1.4753e-01,\n",
+      "           4.4653e-02,  1.9245e-02,  3.8093e-02,  1.0158e-01, -4.3646e-02,\n",
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+      "          -0.0908, -0.1317,  0.1192,  0.4313, -0.0851, -0.1807,  0.1204,\n",
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+      "          -0.0990,  0.1090,  0.1267,  0.2578,  0.0501, -0.1733, -0.1170,\n",
+      "          -0.1403, -0.4787,  0.1272, -0.0632,  0.0988,  0.3264,  0.1055,\n",
+      "           0.0343,  0.0405, -0.0056,  0.1102, -0.2660, -0.1654,  0.1530,\n",
+      "           0.0636, -0.0223,  0.1924, -0.2318, -0.1286,  0.2206,  0.1257,\n",
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+      "           0.0282, -0.1494,  0.0187, -0.0855, -0.2410, -0.1237,  0.0555,\n",
+      "           0.1439,  0.1793,  0.0084, -0.0185],\n",
+      "         [ 0.3369, -0.1479, -0.5276,  0.1676, -0.1363,  0.0398,  0.1985,\n",
+      "           0.2396, -0.1705,  0.1846,  0.0800,  0.0618, -0.0620,  0.2784,\n",
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+      "          -0.0118, -0.1135,  0.0777, -0.1381,  0.1587,  0.2140, -0.0439,\n",
+      "          -0.3028,  0.1019,  0.0596, -0.1013, -0.0633,  0.1042,  0.0591,\n",
+      "          -0.0395,  0.0779, -0.0082, -0.0133, -0.0734,  0.0098,  0.0712,\n",
+      "           0.1292, -0.3829,  0.0417,  0.2043, -0.1393,  0.2560,  0.1654,\n",
+      "          -0.0879,  0.2017,  0.0713, -0.0830,  0.1072,  0.1062,  0.2560,\n",
+      "          -0.4510,  0.1106,  0.1474,  0.1427, -0.0086,  0.3868,  0.3241,\n",
+      "           0.4367,  0.0656,  0.0621, -0.1387,  0.1025, -0.0375, -0.2811,\n",
+      "          -0.0774, -0.0591, -0.3121,  0.1357,  0.3206,  0.0585, -0.2302,\n",
+      "          -0.0596, -0.2986, -0.0889, -0.0869, -0.1366,  0.0568,  0.0286,\n",
+      "          -0.1777, -0.0427, -0.1456, -0.1354,  0.0482, -0.3316, -0.1904,\n",
+      "          -0.4873, -0.5369, -0.1222,  0.0694, -0.0939, -0.1674,  0.0686,\n",
+      "           0.0854,  0.1132,  0.2842, -0.1667,  0.0033,  0.0907,  0.1604,\n",
+      "          -0.2291, -0.2927,  0.3881,  0.0979,  0.4785,  0.1536, -0.4774,\n",
+      "          -0.2153,  0.1427, -0.1574,  0.1796,  0.3557, -0.0449, -0.0526,\n",
+      "          -0.3581, -0.0234, -0.3326,  0.0049,  0.0933, -0.3071, -0.1452,\n",
+      "          -0.2091, -0.0060, -0.0311, -0.0465,  0.1057,  0.1421, -0.0608,\n",
+      "          -0.1065, -0.0965, -0.1038, -0.3565,  0.0803,  0.0465,  0.2869,\n",
+      "          -0.0856,  0.3659,  0.1754, -0.3457,  0.0169, -0.2568,  0.2813,\n",
+      "          -0.0543,  0.0783,  0.1306, -0.2800, -0.1639,  0.2811,  0.0763,\n",
+      "          -0.1103, -0.4329,  0.1929, -0.0220, -0.0827,  0.2655,  0.0324,\n",
+      "           0.0027,  0.0717,  0.2442,  0.0713,  0.1201,  0.2816, -0.0332,\n",
+      "           0.2074, -0.1304, -0.0855, -0.3259, -0.0834,  0.3056,  0.0815,\n",
+      "          -0.1666, -0.1157, -0.0828, -0.2060,  0.0282, -0.0599,  0.0598,\n",
+      "           0.0145,  0.2333, -0.1539,  0.4261]]], grad_fn=<StackBackward0>)))\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient for fc layer:  torch.Size([3, 5, 5000])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# After training, you can analyze the activations and gradients\n",
+    "# Example: Plot the activations of the LSTM layer\n",
+    "layer_name, activation = activations[0]  # Assuming lstm activation\n",
+    "print(f\"Activation for {layer_name} layer: \", activation)\n",
+    "\n",
+    "# Plotting histogram of activations of the LSTM layer\n",
+    "plt.hist(activation[0].detach().numpy().flatten(), bins=100, alpha=0.7, color='b', label='LSTM Activations')\n",
+    "plt.title(f\"Activation Distribution for {layer_name}\")\n",
+    "plt.xlabel(\"Activation Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Example: Plot the gradients of the LSTM layer\n",
+    "layer_name, grad = gradients[0]  # Assuming lstm gradient\n",
+    "print(f\"Gradient for {layer_name} layer: \", grad[0].shape)\n",
+    "\n",
+    "# Plotting histogram of gradients of the LSTM layer\n",
+    "plt.hist(grad[0].numpy().flatten(), bins=100, alpha=0.7, color='r', label='LSTM Gradients')\n",
+    "plt.title(f\"Gradient Distribution for {layer_name}\")\n",
+    "plt.xlabel(\"Gradient Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Activation for fc layer:  torch.Size([3, 5, 5000])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient for lstm layer:  torch.Size([3, 5, 256])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Example: Plot the activations of the final Linear layer\n",
+    "layer_name, activation = activations[1]  # Assuming fc activation\n",
+    "print(f\"Activation for {layer_name} layer: \", activation.shape)\n",
+    "\n",
+    "# Plotting histogram of activations of the final Linear layer\n",
+    "plt.hist(activation.detach().numpy().flatten(), bins=100, alpha=0.7, color='g', label='FC Activations')\n",
+    "plt.title(f\"Activation Distribution for {layer_name}\")\n",
+    "plt.xlabel(\"Activation Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Example: Plot the gradients of the final Linear layer\n",
+    "layer_name, grad = gradients[1]  # Assuming fc gradient\n",
+    "print(f\"Gradient for {layer_name} layer: \", grad[0].shape)\n",
+    "\n",
+    "# Plotting histogram of gradients of the final Linear layer\n",
+    "plt.hist(grad[0].numpy().flatten(), bins=100, alpha=0.7, color='y', label='FC Gradients')\n",
+    "plt.title(f\"Gradient Distribution for {layer_name}\")\n",
+    "plt.xlabel(\"Gradient Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neptune_scale_py_312_base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From d03fd4318cf7c87807576cd5d49b979ed1440254 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 14:45:49 +0100
Subject: [PATCH 014/125] refactor: add validation and test loss calcualtion
 for each epoch

---
 .../pytorch_text_model_debugging.ipynb        | 365 +++++++++++++++---
 1 file changed, 317 insertions(+), 48 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 4c8f180..0561123 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,7 +10,56 @@
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n"
+    "import numpy as np\n",
+    "\n",
+    "from neptune_scale import Run\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    \"optimizer\": \"Adam\",\n",
+    "    \"batch_size\": 256,\n",
+    "    \"learning_rate\": 0.01,\n",
+    "    \"epochs\": 400, \n",
+    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+    "    \"input_features\": 256,\n",
+    "    \"n_classes\": 10,\n",
+    "    \"input_size\": 28 * 28\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Neptune parameters\n",
+    "from neptune_scale import Run\n",
+    "from uuid import uuid4\n",
+    "\n",
+    "run = Run(\n",
+    "    project = \"leo/pytorch-tutorial\",\n",
+    "    run_id=f\"pytorch-text-{uuid4()}\"\n",
+    "    )\n",
+    "\n",
+    "run.log_configs(\n",
+    "    {\n",
+    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
+    "        \"config/optimizer\": params[\"optimizer\"],\n",
+    "        \"config/batch_size\": params[\"batch_size\"],\n",
+    "        \"config/epochs\": params[\"epochs\"],\n",
+    "        \"config/input_size\": params[\"input_size\"]\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
+    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
    ]
   },
   {
@@ -19,7 +68,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO - capture logs to Neptune\n",
     "# TODO - improve dataset to be more realistic\n",
     "# TODO - capture the folowings; attentions, activations, gradients, weights, loss (agg and per layer), learning rate (per layer if needed)\n",
     "# TODO - capture per layer losses\n",
@@ -71,46 +119,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_model(model, input, target, vocab_size):\n",
+    "    model.eval() # set model to evaluation mode\n",
+    "    with torch.no_grad(): # disable gradient calculation\n",
+    "        output = model(input)\n",
+    "        output = output.view(-1, vocab_size)\n",
+    "        data_target = target.view(-1)\n",
+    "        criterion = nn.CrossEntropyLoss()\n",
+    "        loss = criterion(output, data_target)\n",
+    "        \n",
+    "        return loss.item()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/400, Loss: 11.5184\n",
-      "Epoch 2/400, Loss: 11.0458\n",
-      "Epoch 3/400, Loss: 10.6166\n",
-      "Epoch 4/400, Loss: 10.2209\n",
-      "Epoch 5/400, Loss: 9.8457\n",
-      "Epoch 6/400, Loss: 9.4807\n",
-      "Epoch 7/400, Loss: 9.1192\n",
-      "Epoch 8/400, Loss: 8.7570\n",
-      "Epoch 9/400, Loss: 8.3914\n",
-      "Epoch 10/400, Loss: 8.0211\n",
-      "Epoch 11/400, Loss: 7.6454\n",
-      "Epoch 12/400, Loss: 7.2639\n",
-      "Epoch 13/400, Loss: 6.8763\n",
-      "Epoch 14/400, Loss: 6.4826\n",
-      "Epoch 15/400, Loss: 6.0829\n",
-      "Epoch 16/400, Loss: 5.6777\n",
-      "Epoch 17/400, Loss: 5.2678\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[7], line 66\u001b[0m\n\u001b[0;32m     63\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(output, target_data)\n\u001b[0;32m     65\u001b[0m \u001b[38;5;66;03m# Backward pass\u001b[39;00m\n\u001b[1;32m---> 66\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[0;32m     69\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "data": {
+      "text/plain": [
+       "<torch.utils.hooks.RemovableHandle at 0x1c7bd9288c0>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -142,9 +181,12 @@
     "    return hook\n",
     "\n",
     "# Initialize model, loss function, and optimizer\n",
-    "vocab_size = 100000  # Just an example vocab size\n",
+    "vocab_size = 200000  # Just an example vocab size\n",
     "embed_size = 1000  # Embedding size\n",
     "hidden_size = 256  # Hidden size for the LSTM\n",
+    "batch_size = 3\n",
+    "sequence_length = 5\n",
+    "\n",
     "model = SimpleLLM(vocab_size, embed_size, hidden_size)\n",
     "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
     "criterion = nn.CrossEntropyLoss()\n",
@@ -158,11 +200,225 @@
     "model.fc.register_forward_hook(capture_activation('fc'))\n",
     "\n",
     "model.lstm.register_full_backward_hook(capture_gradient('lstm'))\n",
-    "model.fc.register_full_backward_hook(capture_gradient('fc'))\n",
+    "model.fc.register_full_backward_hook(capture_gradient('fc'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "# Dummy input data (example with batch_size=3 and sequence_length=5)\n",
-    "input_data = torch.randint(0, vocab_size, (5, 10))  # Random word indices\n",
-    "target_data = torch.randint(0, vocab_size, (5, 10))  # Random target labels\n",
+    "input_data = torch.randint(0, vocab_size, (batch_size * 3, sequence_length))  # Random word indices\n",
+    "target_data = torch.randint(0, vocab_size, (batch_size * 3, sequence_length))  # Random target labels\n",
+    "\n",
+    "# Manually splitting the data into train, validation, and test sets\n",
+    "train_size = int(0.6 * input_data.size(0))  # 60% for training\n",
+    "val_size = int(0.2 * input_data.size(0))    # 20% for validation\n",
+    "test_size = input_data.size(0) - train_size - val_size  # Remaining 20% for testing\n",
+    "\n",
+    "# Creating the splits manually\n",
+    "train_input = input_data[:train_size]\n",
+    "train_target = target_data[:train_size]\n",
+    "\n",
+    "val_input = input_data[train_size:train_size + val_size]\n",
+    "val_target = target_data[train_size:train_size + val_size]\n",
+    "\n",
+    "test_input = input_data[train_size + val_size:]\n",
+    "test_target = target_data[train_size + val_size:]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/400, Loss: 12.2419\n",
+      "Epoch 2/400, Loss: 11.6387\n",
+      "Epoch 3/400, Loss: 11.1144\n",
+      "Epoch 4/400, Loss: 10.6412\n",
+      "Epoch 5/400, Loss: 10.1949\n",
+      "Epoch 6/400, Loss: 9.7637\n",
+      "Epoch 7/400, Loss: 9.3417\n",
+      "Epoch 8/400, Loss: 8.9259\n",
+      "Epoch 9/400, Loss: 8.5148\n",
+      "Epoch 10/400, Loss: 8.1072\n",
+      "Epoch 11/400, Loss: 7.7022\n",
+      "Epoch 12/400, Loss: 7.2992\n",
+      "Epoch 13/400, Loss: 6.8982\n",
+      "Epoch 14/400, Loss: 6.4999\n",
+      "Epoch 15/400, Loss: 6.1048\n",
+      "Epoch 16/400, Loss: 5.7135\n",
+      "Epoch 17/400, Loss: 5.3260\n",
+      "Epoch 18/400, Loss: 4.9422\n",
+      "Epoch 19/400, Loss: 4.5620\n",
+      "Epoch 20/400, Loss: 4.1852\n",
+      "Epoch 21/400, Loss: 3.8126\n",
+      "Epoch 22/400, Loss: 3.4462\n",
+      "Epoch 23/400, Loss: 3.0892\n",
+      "Epoch 24/400, Loss: 2.7456\n",
+      "Epoch 25/400, Loss: 2.4194\n",
+      "Epoch 26/400, Loss: 2.1149\n",
+      "Epoch 27/400, Loss: 1.8359\n",
+      "Epoch 28/400, Loss: 1.5853\n",
+      "Epoch 29/400, Loss: 1.3643\n",
+      "Epoch 30/400, Loss: 1.1723\n",
+      "Epoch 31/400, Loss: 1.0070\n",
+      "Epoch 32/400, Loss: 0.8651\n",
+      "Epoch 33/400, Loss: 0.7432\n",
+      "Epoch 34/400, Loss: 0.6379\n",
+      "Epoch 35/400, Loss: 0.5465\n",
+      "Epoch 36/400, Loss: 0.4668\n",
+      "Epoch 37/400, Loss: 0.3975\n",
+      "Epoch 38/400, Loss: 0.3371\n",
+      "Epoch 39/400, Loss: 0.2847\n",
+      "Epoch 40/400, Loss: 0.2394\n",
+      "Epoch 41/400, Loss: 0.2005\n",
+      "Epoch 42/400, Loss: 0.1674\n",
+      "Epoch 43/400, Loss: 0.1395\n",
+      "Epoch 44/400, Loss: 0.1162\n",
+      "Epoch 45/400, Loss: 0.0969\n",
+      "Epoch 46/400, Loss: 0.0810\n",
+      "Epoch 47/400, Loss: 0.0681\n",
+      "Epoch 48/400, Loss: 0.0577\n",
+      "Epoch 49/400, Loss: 0.0492\n",
+      "Epoch 50/400, Loss: 0.0423\n",
+      "Epoch 51/400, Loss: 0.0367\n",
+      "Epoch 52/400, Loss: 0.0321\n",
+      "Epoch 53/400, Loss: 0.0283\n",
+      "Epoch 54/400, Loss: 0.0252\n",
+      "Epoch 55/400, Loss: 0.0227\n",
+      "Epoch 56/400, Loss: 0.0205\n",
+      "Epoch 57/400, Loss: 0.0187\n",
+      "Epoch 58/400, Loss: 0.0172\n",
+      "Epoch 59/400, Loss: 0.0159\n",
+      "Epoch 60/400, Loss: 0.0148\n",
+      "Epoch 61/400, Loss: 0.0138\n",
+      "Epoch 62/400, Loss: 0.0130\n",
+      "Epoch 63/400, Loss: 0.0123\n",
+      "Epoch 64/400, Loss: 0.0117\n",
+      "Epoch 65/400, Loss: 0.0111\n",
+      "Epoch 66/400, Loss: 0.0107\n",
+      "Epoch 67/400, Loss: 0.0102\n",
+      "Epoch 68/400, Loss: 0.0099\n",
+      "Epoch 69/400, Loss: 0.0095\n",
+      "Epoch 70/400, Loss: 0.0092\n",
+      "Epoch 71/400, Loss: 0.0090\n",
+      "Epoch 72/400, Loss: 0.0087\n",
+      "Epoch 73/400, Loss: 0.0085\n",
+      "Epoch 74/400, Loss: 0.0083\n",
+      "Epoch 75/400, Loss: 0.0081\n",
+      "Epoch 76/400, Loss: 0.0079\n",
+      "Epoch 77/400, Loss: 0.0078\n",
+      "Epoch 78/400, Loss: 0.0076\n",
+      "Epoch 79/400, Loss: 0.0075\n",
+      "Epoch 80/400, Loss: 0.0074\n",
+      "Epoch 81/400, Loss: 0.0072\n",
+      "Epoch 82/400, Loss: 0.0071\n",
+      "Epoch 83/400, Loss: 0.0070\n",
+      "Epoch 84/400, Loss: 0.0069\n",
+      "Epoch 85/400, Loss: 0.0068\n",
+      "Epoch 86/400, Loss: 0.0067\n",
+      "Epoch 87/400, Loss: 0.0067\n",
+      "Epoch 88/400, Loss: 0.0066\n",
+      "Epoch 89/400, Loss: 0.0065\n",
+      "Epoch 90/400, Loss: 0.0064\n",
+      "Epoch 91/400, Loss: 0.0064\n",
+      "Epoch 92/400, Loss: 0.0063\n",
+      "Epoch 93/400, Loss: 0.0062\n",
+      "Epoch 94/400, Loss: 0.0062\n",
+      "Epoch 95/400, Loss: 0.0061\n",
+      "Epoch 96/400, Loss: 0.0060\n",
+      "Epoch 97/400, Loss: 0.0060\n",
+      "Epoch 98/400, Loss: 0.0059\n",
+      "Epoch 99/400, Loss: 0.0059\n",
+      "Epoch 100/400, Loss: 0.0058\n",
+      "Epoch 101/400, Loss: 0.0058\n",
+      "Epoch 102/400, Loss: 0.0057\n",
+      "Epoch 103/400, Loss: 0.0057\n",
+      "Epoch 104/400, Loss: 0.0056\n",
+      "Epoch 105/400, Loss: 0.0056\n",
+      "Epoch 106/400, Loss: 0.0056\n",
+      "Epoch 107/400, Loss: 0.0055\n",
+      "Epoch 108/400, Loss: 0.0055\n",
+      "Epoch 109/400, Loss: 0.0054\n",
+      "Epoch 110/400, Loss: 0.0054\n",
+      "Epoch 111/400, Loss: 0.0053\n",
+      "Epoch 112/400, Loss: 0.0053\n",
+      "Epoch 113/400, Loss: 0.0053\n",
+      "Epoch 114/400, Loss: 0.0052\n",
+      "Epoch 115/400, Loss: 0.0052\n",
+      "Epoch 116/400, Loss: 0.0052\n",
+      "Epoch 117/400, Loss: 0.0051\n",
+      "Epoch 118/400, Loss: 0.0051\n",
+      "Epoch 119/400, Loss: 0.0050\n",
+      "Epoch 120/400, Loss: 0.0050\n",
+      "Epoch 121/400, Loss: 0.0050\n",
+      "Epoch 122/400, Loss: 0.0049\n",
+      "Epoch 123/400, Loss: 0.0049\n",
+      "Epoch 124/400, Loss: 0.0049\n",
+      "Epoch 125/400, Loss: 0.0048\n",
+      "Epoch 126/400, Loss: 0.0048\n",
+      "Epoch 127/400, Loss: 0.0048\n",
+      "Epoch 128/400, Loss: 0.0047\n",
+      "Epoch 129/400, Loss: 0.0047\n",
+      "Epoch 130/400, Loss: 0.0047\n",
+      "Epoch 131/400, Loss: 0.0047\n",
+      "Epoch 132/400, Loss: 0.0046\n",
+      "Epoch 133/400, Loss: 0.0046\n",
+      "Epoch 134/400, Loss: 0.0046\n",
+      "Epoch 135/400, Loss: 0.0045\n",
+      "Epoch 136/400, Loss: 0.0045\n",
+      "Epoch 137/400, Loss: 0.0045\n",
+      "Epoch 138/400, Loss: 0.0045\n",
+      "Epoch 139/400, Loss: 0.0044\n",
+      "Epoch 140/400, Loss: 0.0044\n",
+      "Epoch 141/400, Loss: 0.0044\n",
+      "Epoch 142/400, Loss: 0.0043\n",
+      "Epoch 143/400, Loss: 0.0043\n",
+      "Epoch 144/400, Loss: 0.0043\n",
+      "Epoch 145/400, Loss: 0.0043\n",
+      "Epoch 146/400, Loss: 0.0042\n",
+      "Epoch 147/400, Loss: 0.0042\n",
+      "Epoch 148/400, Loss: 0.0042\n",
+      "Epoch 149/400, Loss: 0.0042\n",
+      "Epoch 150/400, Loss: 0.0041\n",
+      "Epoch 151/400, Loss: 0.0041\n",
+      "Epoch 152/400, Loss: 0.0041\n",
+      "Epoch 153/400, Loss: 0.0041\n",
+      "Epoch 154/400, Loss: 0.0040\n",
+      "Epoch 155/400, Loss: 0.0040\n",
+      "Epoch 156/400, Loss: 0.0040\n",
+      "Epoch 157/400, Loss: 0.0040\n",
+      "Epoch 158/400, Loss: 0.0040\n",
+      "Epoch 159/400, Loss: 0.0039\n",
+      "Epoch 160/400, Loss: 0.0039\n",
+      "Epoch 161/400, Loss: 0.0039\n",
+      "Epoch 162/400, Loss: 0.0039\n",
+      "Epoch 163/400, Loss: 0.0038\n",
+      "Epoch 164/400, Loss: 0.0038\n",
+      "Epoch 165/400, Loss: 0.0038\n",
+      "Epoch 166/400, Loss: 0.0038\n",
+      "Epoch 167/400, Loss: 0.0038\n",
+      "Epoch 168/400, Loss: 0.0037\n",
+      "Epoch 169/400, Loss: 0.0037\n",
+      "Epoch 170/400, Loss: 0.0037\n",
+      "Epoch 171/400, Loss: 0.0037\n",
+      "Epoch 172/400, Loss: 0.0037\n",
+      "Epoch 173/400, Loss: 0.0036\n",
+      "Epoch 174/400, Loss: 0.0036\n",
+      "Epoch 175/400, Loss: 0.0036\n",
+      "Epoch 176/400, Loss: 0.0036\n"
+     ]
+    }
+   ],
+   "source": [
     "\n",
     "# Training loop\n",
     "num_epochs = 400\n",
@@ -170,11 +426,11 @@
     "    optimizer.zero_grad()\n",
     "\n",
     "    # Forward pass\n",
-    "    output = model(input_data)\n",
+    "    output = model(train_input)\n",
     "    \n",
     "    # Reshape output and target to match the shape of CrossEntropyLoss expectations\n",
     "    output = output.view(-1, vocab_size)  # Flatten the output\n",
-    "    target_data = target_data.view(-1)    # Flatten the target data\n",
+    "    target_data = train_target.view(-1)    # Flatten the target data\n",
     "    \n",
     "    # Calculate loss\n",
     "    loss = criterion(output, target_data)\n",
@@ -185,9 +441,22 @@
     "    # Optimizer step\n",
     "    optimizer.step()\n",
     "\n",
+    "    val_loss = evaluate_model(model, val_input, val_target, vocab_size)\n",
+    "\n",
     "    # Output loss for this epoch\n",
-    "    # if epoch % 50 == 0:\n",
-    "    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n"
+    "    run.log_metrics(\n",
+    "        data = {\n",
+    "            \"training/loss\": loss.item(),\n",
+    "            \"validation/loss\": val_loss\n",
+    "        },\n",
+    "        step = epoch\n",
+    "    )\n",
+    "    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n",
+    "\n",
+    "test_loss = evaluate_model(model, test_input, test_target, vocab_size)\n",
+    "print(f'Test Loss: {test_loss:.4f}')\n",
+    "\n",
+    "run.close()"
    ]
   },
   {

From b7f8e086784d4be068dd3a60e818ff7e301184de Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 14:59:04 +0100
Subject: [PATCH 015/125] refactor: update configs and parameters

---
 .../pytorch_text_model_debugging.ipynb        | 325 ++++++------------
 1 file changed, 105 insertions(+), 220 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 0561123..5aa5e7b 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,25 +17,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# TODO - improve dataset to be more realistic\n",
+    "# TODO - capture the folowings; attentions, activations, gradients, weights, loss (agg and per layer), learning rate (per layer if needed)\n",
+    "# TODO - capture per layer losses\n",
+    "# TODO - increase number of layers\n",
+    "# TODO - Adding dropout to demonstrate the effects of regularization\n",
+    "# TODO - batch normalization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize model, loss function, and optimizer\n",
+    "\n",
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 256,\n",
-    "    \"learning_rate\": 0.01,\n",
+    "    \"batch_size\": 3,\n",
+    "    \"sequence_length\": 5,\n",
+    "    \"learning_rate\": 0.001,\n",
     "    \"epochs\": 400, \n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
-    "    \"n_classes\": 10,\n",
-    "    \"input_size\": 28 * 28\n",
+    "    \"n_classes\": 10, # TODO - remove\n",
+    "    \"input_size\": 28 * 28, # TODO - remove\n",
+    "    \"vocab_size\": 200000,\n",
+    "    \"embed_size\": 1000,\n",
+    "    \"hidden_size\": 256 # hidden size for the LSTM\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,20 +82,6 @@
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO - improve dataset to be more realistic\n",
-    "# TODO - capture the folowings; attentions, activations, gradients, weights, loss (agg and per layer), learning rate (per layer if needed)\n",
-    "# TODO - capture per layer losses\n",
-    "# TODO - increase number of layers\n",
-    "# TODO - Adding dropout to demonstrate the effects of regularization\n",
-    "# TODO - batch normalization"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -119,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,16 +144,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<torch.utils.hooks.RemovableHandle at 0x1c7bd9288c0>"
+       "<torch.utils.hooks.RemovableHandle at 0x1f78d69c650>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,15 +186,8 @@
     "        gradients.append((name, grad_output))  # Capture gradients\n",
     "    return hook\n",
     "\n",
-    "# Initialize model, loss function, and optimizer\n",
-    "vocab_size = 200000  # Just an example vocab size\n",
-    "embed_size = 1000  # Embedding size\n",
-    "hidden_size = 256  # Hidden size for the LSTM\n",
-    "batch_size = 3\n",
-    "sequence_length = 5\n",
-    "\n",
-    "model = SimpleLLM(vocab_size, embed_size, hidden_size)\n",
-    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"])\n",
+    "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
     "criterion = nn.CrossEntropyLoss()\n",
     "\n",
     "# Container for capturing activations and gradients\n",
@@ -205,14 +204,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
     "# Dummy input data (example with batch_size=3 and sequence_length=5)\n",
-    "input_data = torch.randint(0, vocab_size, (batch_size * 3, sequence_length))  # Random word indices\n",
-    "target_data = torch.randint(0, vocab_size, (batch_size * 3, sequence_length))  # Random target labels\n",
+    "input_data = torch.randint(0, params[\"vocab_size\"], (params[\"batch_size\"] * 3, params[\"sequence_length\"]))  # Random word indices\n",
+    "target_data = torch.randint(0, params[\"vocab_size\"], (params[\"batch_size\"] * 3, params[\"sequence_length\"]))  # Random target labels\n",
     "\n",
     "# Manually splitting the data into train, validation, and test sets\n",
     "train_size = int(0.6 * input_data.size(0))  # 60% for training\n",
@@ -239,197 +238,65 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/400, Loss: 12.2419\n",
-      "Epoch 2/400, Loss: 11.6387\n",
-      "Epoch 3/400, Loss: 11.1144\n",
-      "Epoch 4/400, Loss: 10.6412\n",
-      "Epoch 5/400, Loss: 10.1949\n",
-      "Epoch 6/400, Loss: 9.7637\n",
-      "Epoch 7/400, Loss: 9.3417\n",
-      "Epoch 8/400, Loss: 8.9259\n",
-      "Epoch 9/400, Loss: 8.5148\n",
-      "Epoch 10/400, Loss: 8.1072\n",
-      "Epoch 11/400, Loss: 7.7022\n",
-      "Epoch 12/400, Loss: 7.2992\n",
-      "Epoch 13/400, Loss: 6.8982\n",
-      "Epoch 14/400, Loss: 6.4999\n",
-      "Epoch 15/400, Loss: 6.1048\n",
-      "Epoch 16/400, Loss: 5.7135\n",
-      "Epoch 17/400, Loss: 5.3260\n",
-      "Epoch 18/400, Loss: 4.9422\n",
-      "Epoch 19/400, Loss: 4.5620\n",
-      "Epoch 20/400, Loss: 4.1852\n",
-      "Epoch 21/400, Loss: 3.8126\n",
-      "Epoch 22/400, Loss: 3.4462\n",
-      "Epoch 23/400, Loss: 3.0892\n",
-      "Epoch 24/400, Loss: 2.7456\n",
-      "Epoch 25/400, Loss: 2.4194\n",
-      "Epoch 26/400, Loss: 2.1149\n",
-      "Epoch 27/400, Loss: 1.8359\n",
-      "Epoch 28/400, Loss: 1.5853\n",
-      "Epoch 29/400, Loss: 1.3643\n",
-      "Epoch 30/400, Loss: 1.1723\n",
-      "Epoch 31/400, Loss: 1.0070\n",
-      "Epoch 32/400, Loss: 0.8651\n",
-      "Epoch 33/400, Loss: 0.7432\n",
-      "Epoch 34/400, Loss: 0.6379\n",
-      "Epoch 35/400, Loss: 0.5465\n",
-      "Epoch 36/400, Loss: 0.4668\n",
-      "Epoch 37/400, Loss: 0.3975\n",
-      "Epoch 38/400, Loss: 0.3371\n",
-      "Epoch 39/400, Loss: 0.2847\n",
-      "Epoch 40/400, Loss: 0.2394\n",
-      "Epoch 41/400, Loss: 0.2005\n",
-      "Epoch 42/400, Loss: 0.1674\n",
-      "Epoch 43/400, Loss: 0.1395\n",
-      "Epoch 44/400, Loss: 0.1162\n",
-      "Epoch 45/400, Loss: 0.0969\n",
-      "Epoch 46/400, Loss: 0.0810\n",
-      "Epoch 47/400, Loss: 0.0681\n",
-      "Epoch 48/400, Loss: 0.0577\n",
-      "Epoch 49/400, Loss: 0.0492\n",
-      "Epoch 50/400, Loss: 0.0423\n",
-      "Epoch 51/400, Loss: 0.0367\n",
-      "Epoch 52/400, Loss: 0.0321\n",
-      "Epoch 53/400, Loss: 0.0283\n",
-      "Epoch 54/400, Loss: 0.0252\n",
-      "Epoch 55/400, Loss: 0.0227\n",
-      "Epoch 56/400, Loss: 0.0205\n",
-      "Epoch 57/400, Loss: 0.0187\n",
-      "Epoch 58/400, Loss: 0.0172\n",
-      "Epoch 59/400, Loss: 0.0159\n",
-      "Epoch 60/400, Loss: 0.0148\n",
-      "Epoch 61/400, Loss: 0.0138\n",
-      "Epoch 62/400, Loss: 0.0130\n",
-      "Epoch 63/400, Loss: 0.0123\n",
-      "Epoch 64/400, Loss: 0.0117\n",
-      "Epoch 65/400, Loss: 0.0111\n",
-      "Epoch 66/400, Loss: 0.0107\n",
-      "Epoch 67/400, Loss: 0.0102\n",
-      "Epoch 68/400, Loss: 0.0099\n",
-      "Epoch 69/400, Loss: 0.0095\n",
-      "Epoch 70/400, Loss: 0.0092\n",
-      "Epoch 71/400, Loss: 0.0090\n",
-      "Epoch 72/400, Loss: 0.0087\n",
-      "Epoch 73/400, Loss: 0.0085\n",
-      "Epoch 74/400, Loss: 0.0083\n",
-      "Epoch 75/400, Loss: 0.0081\n",
-      "Epoch 76/400, Loss: 0.0079\n",
-      "Epoch 77/400, Loss: 0.0078\n",
-      "Epoch 78/400, Loss: 0.0076\n",
-      "Epoch 79/400, Loss: 0.0075\n",
-      "Epoch 80/400, Loss: 0.0074\n",
-      "Epoch 81/400, Loss: 0.0072\n",
-      "Epoch 82/400, Loss: 0.0071\n",
-      "Epoch 83/400, Loss: 0.0070\n",
-      "Epoch 84/400, Loss: 0.0069\n",
-      "Epoch 85/400, Loss: 0.0068\n",
-      "Epoch 86/400, Loss: 0.0067\n",
-      "Epoch 87/400, Loss: 0.0067\n",
-      "Epoch 88/400, Loss: 0.0066\n",
-      "Epoch 89/400, Loss: 0.0065\n",
-      "Epoch 90/400, Loss: 0.0064\n",
-      "Epoch 91/400, Loss: 0.0064\n",
-      "Epoch 92/400, Loss: 0.0063\n",
-      "Epoch 93/400, Loss: 0.0062\n",
-      "Epoch 94/400, Loss: 0.0062\n",
-      "Epoch 95/400, Loss: 0.0061\n",
-      "Epoch 96/400, Loss: 0.0060\n",
-      "Epoch 97/400, Loss: 0.0060\n",
-      "Epoch 98/400, Loss: 0.0059\n",
-      "Epoch 99/400, Loss: 0.0059\n",
-      "Epoch 100/400, Loss: 0.0058\n",
-      "Epoch 101/400, Loss: 0.0058\n",
-      "Epoch 102/400, Loss: 0.0057\n",
-      "Epoch 103/400, Loss: 0.0057\n",
-      "Epoch 104/400, Loss: 0.0056\n",
-      "Epoch 105/400, Loss: 0.0056\n",
-      "Epoch 106/400, Loss: 0.0056\n",
-      "Epoch 107/400, Loss: 0.0055\n",
-      "Epoch 108/400, Loss: 0.0055\n",
-      "Epoch 109/400, Loss: 0.0054\n",
-      "Epoch 110/400, Loss: 0.0054\n",
-      "Epoch 111/400, Loss: 0.0053\n",
-      "Epoch 112/400, Loss: 0.0053\n",
-      "Epoch 113/400, Loss: 0.0053\n",
-      "Epoch 114/400, Loss: 0.0052\n",
-      "Epoch 115/400, Loss: 0.0052\n",
-      "Epoch 116/400, Loss: 0.0052\n",
-      "Epoch 117/400, Loss: 0.0051\n",
-      "Epoch 118/400, Loss: 0.0051\n",
-      "Epoch 119/400, Loss: 0.0050\n",
-      "Epoch 120/400, Loss: 0.0050\n",
-      "Epoch 121/400, Loss: 0.0050\n",
-      "Epoch 122/400, Loss: 0.0049\n",
-      "Epoch 123/400, Loss: 0.0049\n",
-      "Epoch 124/400, Loss: 0.0049\n",
-      "Epoch 125/400, Loss: 0.0048\n",
-      "Epoch 126/400, Loss: 0.0048\n",
-      "Epoch 127/400, Loss: 0.0048\n",
-      "Epoch 128/400, Loss: 0.0047\n",
-      "Epoch 129/400, Loss: 0.0047\n",
-      "Epoch 130/400, Loss: 0.0047\n",
-      "Epoch 131/400, Loss: 0.0047\n",
-      "Epoch 132/400, Loss: 0.0046\n",
-      "Epoch 133/400, Loss: 0.0046\n",
-      "Epoch 134/400, Loss: 0.0046\n",
-      "Epoch 135/400, Loss: 0.0045\n",
-      "Epoch 136/400, Loss: 0.0045\n",
-      "Epoch 137/400, Loss: 0.0045\n",
-      "Epoch 138/400, Loss: 0.0045\n",
-      "Epoch 139/400, Loss: 0.0044\n",
-      "Epoch 140/400, Loss: 0.0044\n",
-      "Epoch 141/400, Loss: 0.0044\n",
-      "Epoch 142/400, Loss: 0.0043\n",
-      "Epoch 143/400, Loss: 0.0043\n",
-      "Epoch 144/400, Loss: 0.0043\n",
-      "Epoch 145/400, Loss: 0.0043\n",
-      "Epoch 146/400, Loss: 0.0042\n",
-      "Epoch 147/400, Loss: 0.0042\n",
-      "Epoch 148/400, Loss: 0.0042\n",
-      "Epoch 149/400, Loss: 0.0042\n",
-      "Epoch 150/400, Loss: 0.0041\n",
-      "Epoch 151/400, Loss: 0.0041\n",
-      "Epoch 152/400, Loss: 0.0041\n",
-      "Epoch 153/400, Loss: 0.0041\n",
-      "Epoch 154/400, Loss: 0.0040\n",
-      "Epoch 155/400, Loss: 0.0040\n",
-      "Epoch 156/400, Loss: 0.0040\n",
-      "Epoch 157/400, Loss: 0.0040\n",
-      "Epoch 158/400, Loss: 0.0040\n",
-      "Epoch 159/400, Loss: 0.0039\n",
-      "Epoch 160/400, Loss: 0.0039\n",
-      "Epoch 161/400, Loss: 0.0039\n",
-      "Epoch 162/400, Loss: 0.0039\n",
-      "Epoch 163/400, Loss: 0.0038\n",
-      "Epoch 164/400, Loss: 0.0038\n",
-      "Epoch 165/400, Loss: 0.0038\n",
-      "Epoch 166/400, Loss: 0.0038\n",
-      "Epoch 167/400, Loss: 0.0038\n",
-      "Epoch 168/400, Loss: 0.0037\n",
-      "Epoch 169/400, Loss: 0.0037\n",
-      "Epoch 170/400, Loss: 0.0037\n",
-      "Epoch 171/400, Loss: 0.0037\n",
-      "Epoch 172/400, Loss: 0.0037\n",
-      "Epoch 173/400, Loss: 0.0036\n",
-      "Epoch 174/400, Loss: 0.0036\n",
-      "Epoch 175/400, Loss: 0.0036\n",
-      "Epoch 176/400, Loss: 0.0036\n"
+      "Epoch 1/400, Loss: 12.2172\n",
+      "Epoch 2/400, Loss: 9.4774\n",
+      "Epoch 3/400, Loss: 6.9212\n",
+      "Epoch 4/400, Loss: 4.5795\n",
+      "Epoch 5/400, Loss: 2.3832\n",
+      "Epoch 6/400, Loss: 0.8342\n",
+      "Epoch 7/400, Loss: 0.2218\n",
+      "Epoch 8/400, Loss: 0.0458\n",
+      "Epoch 9/400, Loss: 0.0089\n",
+      "Epoch 10/400, Loss: 0.0020\n",
+      "Epoch 11/400, Loss: 0.0006\n",
+      "Epoch 12/400, Loss: 0.0002\n",
+      "Epoch 13/400, Loss: 0.0001\n",
+      "Epoch 14/400, Loss: 0.0001\n",
+      "Epoch 15/400, Loss: 0.0000\n",
+      "Epoch 16/400, Loss: 0.0000\n",
+      "Epoch 17/400, Loss: 0.0000\n",
+      "Epoch 18/400, Loss: 0.0000\n",
+      "Epoch 19/400, Loss: 0.0000\n",
+      "Epoch 20/400, Loss: 0.0000\n",
+      "Epoch 21/400, Loss: 0.0000\n",
+      "Epoch 22/400, Loss: 0.0000\n",
+      "Epoch 23/400, Loss: 0.0000\n",
+      "Epoch 24/400, Loss: 0.0000\n",
+      "Epoch 25/400, Loss: 0.0000\n",
+      "Epoch 26/400, Loss: 0.0000\n",
+      "Epoch 27/400, Loss: 0.0000\n",
+      "Epoch 28/400, Loss: 0.0000\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 19\u001b[0m\n\u001b[0;32m     16\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     21\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m evaluate_model(model, val_input, val_target, params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvocab_size\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     23\u001b[0m \u001b[38;5;66;03m# Output loss for this epoch\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:425\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m    423\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mlerp_(grad, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m device_beta1)\n\u001b[1;32m--> 425\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maddcmul_(grad, grad\u001b[38;5;241m.\u001b[39mconj(), value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta2)\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[0;32m    428\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
     "\n",
     "# Training loop\n",
-    "num_epochs = 400\n",
-    "for epoch in range(num_epochs):\n",
+    "for epoch in range(params[\"epochs\"]):\n",
     "    optimizer.zero_grad()\n",
     "\n",
     "    # Forward pass\n",
     "    output = model(train_input)\n",
     "    \n",
     "    # Reshape output and target to match the shape of CrossEntropyLoss expectations\n",
-    "    output = output.view(-1, vocab_size)  # Flatten the output\n",
+    "    output = output.view(-1, params[\"vocab_size\"])  # Flatten the output\n",
     "    target_data = train_target.view(-1)    # Flatten the target data\n",
     "    \n",
     "    # Calculate loss\n",
@@ -441,7 +308,7 @@
     "    # Optimizer step\n",
     "    optimizer.step()\n",
     "\n",
-    "    val_loss = evaluate_model(model, val_input, val_target, vocab_size)\n",
+    "    val_loss = evaluate_model(model, val_input, val_target, params[\"vocab_size\"])\n",
     "\n",
     "    # Output loss for this epoch\n",
     "    run.log_metrics(\n",
@@ -451,10 +318,28 @@
     "        },\n",
     "        step = epoch\n",
     "    )\n",
-    "    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n",
+    "    print(f'Epoch {epoch+1}/{params[\"epochs\"]}, Loss: {loss.item():.4f}')\n",
     "\n",
-    "test_loss = evaluate_model(model, test_input, test_target, vocab_size)\n",
-    "print(f'Test Loss: {test_loss:.4f}')\n",
+    "test_loss = evaluate_model(model, test_input, test_target, params[\"vocab_size\"])\n",
+    "print(f'Test Loss: {test_loss:.4f}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "neptune:INFO: Waiting for all operations to be processed\n",
+      "neptune:WARNING: No timeout specified. Waiting indefinitely\n",
+      "neptune:INFO: All operations were processed\n"
+     ]
+    }
+   ],
+   "source": [
     "\n",
     "run.close()"
    ]

From b113a36e1e06b17503c5a600c6d2f901bbb0329a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 15:01:31 +0100
Subject: [PATCH 016/125] refactor: update logged configs

---
 .../pytorch/pytorch_text_model_debugging.ipynb             | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 5aa5e7b..d2ca4da 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,8 +73,11 @@
     "        \"config/learning_rate\": params[\"learning_rate\"],\n",
     "        \"config/optimizer\": params[\"optimizer\"],\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
+    "        \"config/sequece_length\": params[\"sequence_length\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/input_size\": params[\"input_size\"]\n",
+    "        \"config/input_size\": params[\"input_size\"],\n",
+    "        \"data/vocab_size\": params[\"vocab_size\"],\n",
+    "        \"data/embed_size\": params[\"embed_size\"]\n",
     "    }\n",
     ")\n",
     "\n",

From 725899344290c559db7c0b335c5cb99b45bc3ed8 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 15:42:12 +0100
Subject: [PATCH 017/125] refactor: calculate activations per layer

---
 .../pytorch_text_model_debugging.ipynb        | 240 +++++++++++++-----
 1 file changed, 180 insertions(+), 60 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index d2ca4da..0f08f79 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,22 +147,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<torch.utils.hooks.RemovableHandle at 0x1f78d69c650>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "\n",
     "# Define the simple LLM model with LSTM\n",
     "class SimpleLLM(nn.Module):\n",
     "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
@@ -170,12 +158,47 @@
     "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
     "        self.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)\n",
     "        self.fc = nn.Linear(hidden_size, vocab_size)\n",
+    "\n",
+    "        # Initialize activations dictionary to store layer activations\n",
+    "        self.activations = {}\n",
+    "\n",
+    "        # Registering hooks to track activations\n",
+    "        self.hooks = []\n",
+    "        self.hooks.append(self.lstm.register_forward_hook(self.save_activation(\"lstm\")))\n",
+    "        self.hooks.append(self.fc.register_forward_hook(self.save_activation(\"fc\")))\n",
+    "\n",
+    "        # Registering hooks to track gradients\n",
+    "\n",
     "        \n",
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
     "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
     "        out = self.fc(lstm_out)\n",
     "        return out\n",
+    "    \n",
+    "    # Function to save activations\n",
+    "    def save_activation(self, name):\n",
+    "        def hook(model, input, output):\n",
+    "            self.activations[name] = output\n",
+    "        return hook\n",
+    "    \n",
+    "    def get_activations(self):\n",
+    "        return self.activations\n",
+    "\n",
+    "    def clear_activations(self):\n",
+    "        self.activations = {}\n",
+    "\n",
+    "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"])\n",
+    "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
+    "criterion = nn.CrossEntropyLoss()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "# Define a function to capture activations\n",
     "def capture_activation(name):\n",
@@ -187,11 +210,33 @@
     "def capture_gradient(name):\n",
     "    def hook(module, grad_input, grad_output):\n",
     "        gradients.append((name, grad_output))  # Capture gradients\n",
-    "    return hook\n",
-    "\n",
-    "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"])\n",
-    "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
-    "criterion = nn.CrossEntropyLoss()\n",
+    "    return hook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    \"optimizer\": \"Adam\",\n",
+    "    \"batch_size\": 256,\n",
+    "    \"learning_rate\": 0.01,\n",
+    "    \"epochs\": 5, \n",
+    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+    "    \"input_features\": 256,\n",
+    "    \"n_classes\": 10,\n",
+    "    \"input_size\": 28 * 28\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "# Container for capturing activations and gradients\n",
     "activations = []\n",
@@ -207,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,41 +279,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/400, Loss: 12.2172\n",
-      "Epoch 2/400, Loss: 9.4774\n",
-      "Epoch 3/400, Loss: 6.9212\n",
-      "Epoch 4/400, Loss: 4.5795\n",
-      "Epoch 5/400, Loss: 2.3832\n",
-      "Epoch 6/400, Loss: 0.8342\n",
-      "Epoch 7/400, Loss: 0.2218\n",
-      "Epoch 8/400, Loss: 0.0458\n",
-      "Epoch 9/400, Loss: 0.0089\n",
-      "Epoch 10/400, Loss: 0.0020\n",
-      "Epoch 11/400, Loss: 0.0006\n",
-      "Epoch 12/400, Loss: 0.0002\n",
-      "Epoch 13/400, Loss: 0.0001\n",
-      "Epoch 14/400, Loss: 0.0001\n",
-      "Epoch 15/400, Loss: 0.0000\n",
-      "Epoch 16/400, Loss: 0.0000\n",
-      "Epoch 17/400, Loss: 0.0000\n",
-      "Epoch 18/400, Loss: 0.0000\n",
-      "Epoch 19/400, Loss: 0.0000\n",
-      "Epoch 20/400, Loss: 0.0000\n",
-      "Epoch 21/400, Loss: 0.0000\n",
-      "Epoch 22/400, Loss: 0.0000\n",
-      "Epoch 23/400, Loss: 0.0000\n",
-      "Epoch 24/400, Loss: 0.0000\n",
-      "Epoch 25/400, Loss: 0.0000\n",
-      "Epoch 26/400, Loss: 0.0000\n",
-      "Epoch 27/400, Loss: 0.0000\n",
-      "Epoch 28/400, Loss: 0.0000\n"
+      "Epoch 1/400, Loss: 11.6612\n",
+      "Epoch 2/400, Loss: 11.1299\n",
+      "Epoch 3/400, Loss: 10.6519\n",
+      "Epoch 4/400, Loss: 10.2014\n",
+      "Epoch 5/400, Loss: 9.7649\n",
+      "Epoch 6/400, Loss: 9.3361\n",
+      "Epoch 7/400, Loss: 8.9123\n",
+      "Epoch 8/400, Loss: 8.4927\n",
+      "Epoch 9/400, Loss: 8.0768\n",
+      "Epoch 10/400, Loss: 7.6644\n",
+      "Epoch 11/400, Loss: 7.2550\n",
+      "Epoch 12/400, Loss: 6.8478\n",
+      "Epoch 13/400, Loss: 6.4424\n",
+      "Epoch 14/400, Loss: 6.0383\n",
+      "Epoch 15/400, Loss: 5.6355\n",
+      "Epoch 16/400, Loss: 5.2345\n",
+      "Epoch 17/400, Loss: 4.8363\n",
+      "Epoch 18/400, Loss: 4.4415\n",
+      "Epoch 19/400, Loss: 4.0512\n",
+      "Epoch 20/400, Loss: 3.6666\n",
+      "Epoch 21/400, Loss: 3.2899\n",
+      "Epoch 22/400, Loss: 2.9234\n",
+      "Epoch 23/400, Loss: 2.5708\n",
+      "Epoch 24/400, Loss: 2.2367\n",
+      "Epoch 25/400, Loss: 1.9260\n",
+      "Epoch 26/400, Loss: 1.6431\n",
+      "Epoch 27/400, Loss: 1.3913\n",
+      "Epoch 28/400, Loss: 1.1719\n",
+      "Epoch 29/400, Loss: 0.9843\n",
+      "Epoch 30/400, Loss: 0.8258\n",
+      "Epoch 31/400, Loss: 0.6929\n",
+      "Epoch 32/400, Loss: 0.5816\n",
+      "Epoch 33/400, Loss: 0.4882\n",
+      "Epoch 34/400, Loss: 0.4094\n",
+      "Epoch 35/400, Loss: 0.3426\n",
+      "Epoch 36/400, Loss: 0.2859\n",
+      "Epoch 37/400, Loss: 0.2378\n",
+      "Epoch 38/400, Loss: 0.1972\n",
+      "Epoch 39/400, Loss: 0.1631\n",
+      "Epoch 40/400, Loss: 0.1346\n",
+      "Epoch 41/400, Loss: 0.1110\n",
+      "Epoch 42/400, Loss: 0.0916\n",
+      "Epoch 43/400, Loss: 0.0759\n",
+      "Epoch 44/400, Loss: 0.0632\n",
+      "Epoch 45/400, Loss: 0.0531\n",
+      "Epoch 46/400, Loss: 0.0450\n",
+      "Epoch 47/400, Loss: 0.0385\n",
+      "Epoch 48/400, Loss: 0.0333\n",
+      "Epoch 49/400, Loss: 0.0291\n",
+      "Epoch 50/400, Loss: 0.0257\n",
+      "Epoch 51/400, Loss: 0.0229\n",
+      "Epoch 52/400, Loss: 0.0206\n",
+      "Epoch 53/400, Loss: 0.0187\n",
+      "Epoch 54/400, Loss: 0.0171\n",
+      "Epoch 55/400, Loss: 0.0158\n",
+      "Epoch 56/400, Loss: 0.0147\n",
+      "Epoch 57/400, Loss: 0.0137\n",
+      "Epoch 58/400, Loss: 0.0129\n",
+      "Epoch 59/400, Loss: 0.0122\n",
+      "Epoch 60/400, Loss: 0.0116\n",
+      "Epoch 61/400, Loss: 0.0110\n",
+      "Epoch 62/400, Loss: 0.0106\n",
+      "Epoch 63/400, Loss: 0.0102\n",
+      "Epoch 64/400, Loss: 0.0098\n",
+      "Epoch 65/400, Loss: 0.0095\n",
+      "Epoch 66/400, Loss: 0.0092\n",
+      "Epoch 67/400, Loss: 0.0089\n",
+      "Epoch 68/400, Loss: 0.0087\n",
+      "Epoch 69/400, Loss: 0.0085\n",
+      "Epoch 70/400, Loss: 0.0083\n",
+      "Epoch 71/400, Loss: 0.0081\n",
+      "Epoch 72/400, Loss: 0.0079\n",
+      "Epoch 73/400, Loss: 0.0078\n",
+      "Epoch 74/400, Loss: 0.0076\n",
+      "Epoch 75/400, Loss: 0.0075\n",
+      "Epoch 76/400, Loss: 0.0074\n",
+      "Epoch 77/400, Loss: 0.0073\n",
+      "Epoch 78/400, Loss: 0.0072\n",
+      "Epoch 79/400, Loss: 0.0071\n",
+      "Epoch 80/400, Loss: 0.0070\n",
+      "Epoch 81/400, Loss: 0.0069\n",
+      "Epoch 82/400, Loss: 0.0068\n",
+      "Epoch 83/400, Loss: 0.0067\n",
+      "Epoch 84/400, Loss: 0.0067\n",
+      "Epoch 85/400, Loss: 0.0066\n",
+      "Epoch 86/400, Loss: 0.0065\n",
+      "Epoch 87/400, Loss: 0.0065\n",
+      "Epoch 88/400, Loss: 0.0064\n",
+      "Epoch 89/400, Loss: 0.0063\n",
+      "Epoch 90/400, Loss: 0.0063\n",
+      "Epoch 91/400, Loss: 0.0062\n"
      ]
     },
     {
@@ -278,22 +386,27 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[9], line 19\u001b[0m\n\u001b[0;32m     16\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     21\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m evaluate_model(model, val_input, val_target, params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvocab_size\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     23\u001b[0m \u001b[38;5;66;03m# Output loss for this epoch\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:425\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m    423\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mlerp_(grad, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m device_beta1)\n\u001b[1;32m--> 425\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maddcmul_(grad, grad\u001b[38;5;241m.\u001b[39mconj(), value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta2)\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[0;32m    428\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
+      "Cell \u001b[1;32mIn[27], line 25\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[0;32m     23\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m---> 25\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_target\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvocab_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Track activations\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, activation \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mget_activations()\u001b[38;5;241m.\u001b[39mitems():\n",
+      "Cell \u001b[1;32mIn[23], line 4\u001b[0m, in \u001b[0;36mevaluate_model\u001b[1;34m(model, input, target, vocab_size)\u001b[0m\n\u001b[0;32m      2\u001b[0m model\u001b[38;5;241m.\u001b[39meval() \u001b[38;5;66;03m# set model to evaluation mode\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad(): \u001b[38;5;66;03m# disable gradient calculation\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size)\n\u001b[0;32m      6\u001b[0m     data_target \u001b[38;5;241m=\u001b[39m target\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "Cell \u001b[1;32mIn[24], line 22\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m     21\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[1;32m---> 22\u001b[0m     lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[0;32m     23\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc(lstm_out)\n\u001b[0;32m     24\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1124\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[1;34m(self, input, hx)\u001b[0m\n\u001b[0;32m   1121\u001b[0m         hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[0;32m   1123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1124\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1125\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1129\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1130\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1131\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1132\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1133\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1134\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1136\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\n\u001b[0;32m   1137\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m   1138\u001b[0m         batch_sizes,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1145\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional,\n\u001b[0;32m   1146\u001b[0m     )\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
+    "activation_dict_mean = {}\n",
+    "activation_dict_std = {}\n",
     "\n",
     "# Training loop\n",
     "for epoch in range(params[\"epochs\"]):\n",
     "    optimizer.zero_grad()\n",
+    "    model.clear_activations()\n",
     "\n",
     "    # Forward pass\n",
     "    output = model(train_input)\n",
@@ -313,11 +426,18 @@
     "\n",
     "    val_loss = evaluate_model(model, val_input, val_target, params[\"vocab_size\"])\n",
     "\n",
+    "    # Track activations\n",
+    "    for name, activation in model.get_activations().items():\n",
+    "        activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
+    "        activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
+    "\n",
     "    # Output loss for this epoch\n",
     "    run.log_metrics(\n",
     "        data = {\n",
     "            \"training/loss\": loss.item(),\n",
-    "            \"validation/loss\": val_loss\n",
+    "            \"validation/loss\": val_loss,\n",
+    "            **activation_dict_mean,\n",
+    "            **activation_dict_std\n",
     "        },\n",
     "        step = epoch\n",
     "    )\n",

From ae330e6b3d11c9c5deb9af53107f190b07c17c28 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 16:01:40 +0100
Subject: [PATCH 018/125] refactor: add tracking for grad norms

---
 .../pytorch_text_model_debugging.ipynb        | 225 +++++++++---------
 1 file changed, 117 insertions(+), 108 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 0f08f79..400e331 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -22,7 +22,8 @@
    "outputs": [],
    "source": [
     "# TODO - improve dataset to be more realistic\n",
-    "# TODO - capture the folowings; attentions, activations, gradients, weights, loss (agg and per layer), learning rate (per layer if needed)\n",
+    "# TODO - capture the folowings; attentions, gradients - weights, loss (agg and per layer), learning rate (per layer if needed)\n",
+    "    # complete - activations, grad norms\n",
     "# TODO - capture per layer losses\n",
     "# TODO - increase number of layers\n",
     "# TODO - Adding dropout to demonstrate the effects of regularization\n",
@@ -55,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,13 +163,14 @@
     "        # Initialize activations dictionary to store layer activations\n",
     "        self.activations = {}\n",
     "\n",
-    "        # Registering hooks to track activations\n",
+    "        ## Registering hooks to track activations\n",
     "        self.hooks = []\n",
     "        self.hooks.append(self.lstm.register_forward_hook(self.save_activation(\"lstm\")))\n",
     "        self.hooks.append(self.fc.register_forward_hook(self.save_activation(\"fc\")))\n",
     "\n",
-    "        # Registering hooks to track gradients\n",
-    "\n",
+    "        ## Registering hooks to track gradients\n",
+    "        self.hooks.append(self.lstm.register_full_backward_hook(self.save_gradient(\"lstm\")))\n",
+    "        self.hooks.append(self.fc.register_full_backward_hook(self.save_gradient(\"fc\")))\n",
     "        \n",
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
@@ -188,6 +190,19 @@
     "    def clear_activations(self):\n",
     "        self.activations = {}\n",
     "\n",
+    "    # Function to save gradients\n",
+    "    def save_gradient(self, name):\n",
+    "        def hook(module, grad_input, grad_output):\n",
+    "            self.gradients[name] = grad_output[0]  # Save gradient output in the dictionary\n",
+    "        return hook\n",
+    "    \n",
+    "    def get_gradients(self):\n",
+    "        return self.gradients\n",
+    "    \n",
+    "    def clear_gradients(self):\n",
+    "        self.gradients = {}\n",
+    "\n",
+    "\n",
     "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"])\n",
     "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
     "criterion = nn.CrossEntropyLoss()\n"
@@ -279,104 +294,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/400, Loss: 11.6612\n",
-      "Epoch 2/400, Loss: 11.1299\n",
-      "Epoch 3/400, Loss: 10.6519\n",
-      "Epoch 4/400, Loss: 10.2014\n",
-      "Epoch 5/400, Loss: 9.7649\n",
-      "Epoch 6/400, Loss: 9.3361\n",
-      "Epoch 7/400, Loss: 8.9123\n",
-      "Epoch 8/400, Loss: 8.4927\n",
-      "Epoch 9/400, Loss: 8.0768\n",
-      "Epoch 10/400, Loss: 7.6644\n",
-      "Epoch 11/400, Loss: 7.2550\n",
-      "Epoch 12/400, Loss: 6.8478\n",
-      "Epoch 13/400, Loss: 6.4424\n",
-      "Epoch 14/400, Loss: 6.0383\n",
-      "Epoch 15/400, Loss: 5.6355\n",
-      "Epoch 16/400, Loss: 5.2345\n",
-      "Epoch 17/400, Loss: 4.8363\n",
-      "Epoch 18/400, Loss: 4.4415\n",
-      "Epoch 19/400, Loss: 4.0512\n",
-      "Epoch 20/400, Loss: 3.6666\n",
-      "Epoch 21/400, Loss: 3.2899\n",
-      "Epoch 22/400, Loss: 2.9234\n",
-      "Epoch 23/400, Loss: 2.5708\n",
-      "Epoch 24/400, Loss: 2.2367\n",
-      "Epoch 25/400, Loss: 1.9260\n",
-      "Epoch 26/400, Loss: 1.6431\n",
-      "Epoch 27/400, Loss: 1.3913\n",
-      "Epoch 28/400, Loss: 1.1719\n",
-      "Epoch 29/400, Loss: 0.9843\n",
-      "Epoch 30/400, Loss: 0.8258\n",
-      "Epoch 31/400, Loss: 0.6929\n",
-      "Epoch 32/400, Loss: 0.5816\n",
-      "Epoch 33/400, Loss: 0.4882\n",
-      "Epoch 34/400, Loss: 0.4094\n",
-      "Epoch 35/400, Loss: 0.3426\n",
-      "Epoch 36/400, Loss: 0.2859\n",
-      "Epoch 37/400, Loss: 0.2378\n",
-      "Epoch 38/400, Loss: 0.1972\n",
-      "Epoch 39/400, Loss: 0.1631\n",
-      "Epoch 40/400, Loss: 0.1346\n",
-      "Epoch 41/400, Loss: 0.1110\n",
-      "Epoch 42/400, Loss: 0.0916\n",
-      "Epoch 43/400, Loss: 0.0759\n",
-      "Epoch 44/400, Loss: 0.0632\n",
-      "Epoch 45/400, Loss: 0.0531\n",
-      "Epoch 46/400, Loss: 0.0450\n",
-      "Epoch 47/400, Loss: 0.0385\n",
-      "Epoch 48/400, Loss: 0.0333\n",
-      "Epoch 49/400, Loss: 0.0291\n",
-      "Epoch 50/400, Loss: 0.0257\n",
-      "Epoch 51/400, Loss: 0.0229\n",
-      "Epoch 52/400, Loss: 0.0206\n",
-      "Epoch 53/400, Loss: 0.0187\n",
-      "Epoch 54/400, Loss: 0.0171\n",
+      "Epoch 1/400, Loss: 11.5970\n",
+      "Epoch 2/400, Loss: 11.0686\n",
+      "Epoch 3/400, Loss: 10.5915\n",
+      "Epoch 4/400, Loss: 10.1422\n",
+      "Epoch 5/400, Loss: 9.7076\n",
+      "Epoch 6/400, Loss: 9.2819\n",
+      "Epoch 7/400, Loss: 8.8626\n",
+      "Epoch 8/400, Loss: 8.4479\n",
+      "Epoch 9/400, Loss: 8.0365\n",
+      "Epoch 10/400, Loss: 7.6276\n",
+      "Epoch 11/400, Loss: 7.2211\n",
+      "Epoch 12/400, Loss: 6.8171\n",
+      "Epoch 13/400, Loss: 6.4153\n",
+      "Epoch 14/400, Loss: 6.0154\n",
+      "Epoch 15/400, Loss: 5.6169\n",
+      "Epoch 16/400, Loss: 5.2196\n",
+      "Epoch 17/400, Loss: 4.8243\n",
+      "Epoch 18/400, Loss: 4.4318\n",
+      "Epoch 19/400, Loss: 4.0431\n",
+      "Epoch 20/400, Loss: 3.6596\n",
+      "Epoch 21/400, Loss: 3.2834\n",
+      "Epoch 22/400, Loss: 2.9174\n",
+      "Epoch 23/400, Loss: 2.5654\n",
+      "Epoch 24/400, Loss: 2.2321\n",
+      "Epoch 25/400, Loss: 1.9223\n",
+      "Epoch 26/400, Loss: 1.6402\n",
+      "Epoch 27/400, Loss: 1.3889\n",
+      "Epoch 28/400, Loss: 1.1696\n",
+      "Epoch 29/400, Loss: 0.9814\n",
+      "Epoch 30/400, Loss: 0.8221\n",
+      "Epoch 31/400, Loss: 0.6882\n",
+      "Epoch 32/400, Loss: 0.5760\n",
+      "Epoch 33/400, Loss: 0.4818\n",
+      "Epoch 34/400, Loss: 0.4025\n",
+      "Epoch 35/400, Loss: 0.3356\n",
+      "Epoch 36/400, Loss: 0.2791\n",
+      "Epoch 37/400, Loss: 0.2315\n",
+      "Epoch 38/400, Loss: 0.1917\n",
+      "Epoch 39/400, Loss: 0.1585\n",
+      "Epoch 40/400, Loss: 0.1311\n",
+      "Epoch 41/400, Loss: 0.1086\n",
+      "Epoch 42/400, Loss: 0.0903\n",
+      "Epoch 43/400, Loss: 0.0755\n",
+      "Epoch 44/400, Loss: 0.0634\n",
+      "Epoch 45/400, Loss: 0.0537\n",
+      "Epoch 46/400, Loss: 0.0457\n",
+      "Epoch 47/400, Loss: 0.0393\n",
+      "Epoch 48/400, Loss: 0.0340\n",
+      "Epoch 49/400, Loss: 0.0297\n",
+      "Epoch 50/400, Loss: 0.0262\n",
+      "Epoch 51/400, Loss: 0.0233\n",
+      "Epoch 52/400, Loss: 0.0209\n",
+      "Epoch 53/400, Loss: 0.0189\n",
+      "Epoch 54/400, Loss: 0.0172\n",
       "Epoch 55/400, Loss: 0.0158\n",
-      "Epoch 56/400, Loss: 0.0147\n",
-      "Epoch 57/400, Loss: 0.0137\n",
-      "Epoch 58/400, Loss: 0.0129\n",
-      "Epoch 59/400, Loss: 0.0122\n",
-      "Epoch 60/400, Loss: 0.0116\n",
-      "Epoch 61/400, Loss: 0.0110\n",
-      "Epoch 62/400, Loss: 0.0106\n",
-      "Epoch 63/400, Loss: 0.0102\n",
-      "Epoch 64/400, Loss: 0.0098\n",
-      "Epoch 65/400, Loss: 0.0095\n",
-      "Epoch 66/400, Loss: 0.0092\n",
-      "Epoch 67/400, Loss: 0.0089\n",
-      "Epoch 68/400, Loss: 0.0087\n",
-      "Epoch 69/400, Loss: 0.0085\n",
-      "Epoch 70/400, Loss: 0.0083\n",
-      "Epoch 71/400, Loss: 0.0081\n",
-      "Epoch 72/400, Loss: 0.0079\n",
-      "Epoch 73/400, Loss: 0.0078\n",
-      "Epoch 74/400, Loss: 0.0076\n",
-      "Epoch 75/400, Loss: 0.0075\n",
-      "Epoch 76/400, Loss: 0.0074\n",
-      "Epoch 77/400, Loss: 0.0073\n",
-      "Epoch 78/400, Loss: 0.0072\n",
-      "Epoch 79/400, Loss: 0.0071\n",
-      "Epoch 80/400, Loss: 0.0070\n",
-      "Epoch 81/400, Loss: 0.0069\n",
-      "Epoch 82/400, Loss: 0.0068\n",
-      "Epoch 83/400, Loss: 0.0067\n",
-      "Epoch 84/400, Loss: 0.0067\n",
-      "Epoch 85/400, Loss: 0.0066\n",
-      "Epoch 86/400, Loss: 0.0065\n",
-      "Epoch 87/400, Loss: 0.0065\n",
-      "Epoch 88/400, Loss: 0.0064\n",
-      "Epoch 89/400, Loss: 0.0063\n",
-      "Epoch 90/400, Loss: 0.0063\n",
-      "Epoch 91/400, Loss: 0.0062\n"
+      "Epoch 56/400, Loss: 0.0146\n",
+      "Epoch 57/400, Loss: 0.0136\n",
+      "Epoch 58/400, Loss: 0.0128\n",
+      "Epoch 59/400, Loss: 0.0120\n",
+      "Epoch 60/400, Loss: 0.0114\n",
+      "Epoch 61/400, Loss: 0.0108\n",
+      "Epoch 62/400, Loss: 0.0103\n",
+      "Epoch 63/400, Loss: 0.0099\n",
+      "Epoch 64/400, Loss: 0.0095\n",
+      "Epoch 65/400, Loss: 0.0091\n",
+      "Epoch 66/400, Loss: 0.0088\n",
+      "Epoch 67/400, Loss: 0.0086\n",
+      "Epoch 68/400, Loss: 0.0083\n",
+      "Epoch 69/400, Loss: 0.0081\n",
+      "Epoch 70/400, Loss: 0.0079\n",
+      "Epoch 71/400, Loss: 0.0077\n",
+      "Epoch 72/400, Loss: 0.0075\n",
+      "Epoch 73/400, Loss: 0.0074\n",
+      "Epoch 74/400, Loss: 0.0072\n",
+      "Epoch 75/400, Loss: 0.0071\n",
+      "Epoch 76/400, Loss: 0.0070\n",
+      "Epoch 77/400, Loss: 0.0068\n",
+      "Epoch 78/400, Loss: 0.0067\n",
+      "Epoch 79/400, Loss: 0.0066\n"
      ]
     },
     {
@@ -386,15 +389,13 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[27], line 25\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[0;32m     23\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m---> 25\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_target\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvocab_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Track activations\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, activation \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mget_activations()\u001b[38;5;241m.\u001b[39mitems():\n",
-      "Cell \u001b[1;32mIn[23], line 4\u001b[0m, in \u001b[0;36mevaluate_model\u001b[1;34m(model, input, target, vocab_size)\u001b[0m\n\u001b[0;32m      2\u001b[0m model\u001b[38;5;241m.\u001b[39meval() \u001b[38;5;66;03m# set model to evaluation mode\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad(): \u001b[38;5;66;03m# disable gradient calculation\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size)\n\u001b[0;32m      6\u001b[0m     data_target \u001b[38;5;241m=\u001b[39m target\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "Cell \u001b[1;32mIn[24], line 22\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m     21\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[1;32m---> 22\u001b[0m     lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[0;32m     23\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc(lstm_out)\n\u001b[0;32m     24\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1124\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[1;34m(self, input, hx)\u001b[0m\n\u001b[0;32m   1121\u001b[0m         hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[0;32m   1123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1124\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1125\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1129\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1130\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1131\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1132\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1133\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1134\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1136\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\n\u001b[0;32m   1137\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m   1138\u001b[0m         batch_sizes,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1145\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional,\n\u001b[0;32m   1146\u001b[0m     )\n",
+      "Cell \u001b[1;32mIn[36], line 26\u001b[0m\n\u001b[0;32m     23\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     25\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[1;32m---> 26\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     28\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m evaluate_model(model, val_input, val_target, params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvocab_size\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:476\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    474\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (max_exp_avg_sqs[i]\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 476\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (\u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m    478\u001b[0m     param\u001b[38;5;241m.\u001b[39maddcdiv_(exp_avg, denom, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39mstep_size)\n\u001b[0;32m    480\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -402,11 +403,14 @@
    "source": [
     "activation_dict_mean = {}\n",
     "activation_dict_std = {}\n",
+    "grad_norms = {}\n",
     "\n",
     "# Training loop\n",
     "for epoch in range(params[\"epochs\"]):\n",
     "    optimizer.zero_grad()\n",
+    "\n",
     "    model.clear_activations()\n",
+    "    model.clear_gradients()\n",
     "\n",
     "    # Forward pass\n",
     "    output = model(train_input)\n",
@@ -431,13 +435,18 @@
     "        activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
     "        activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
     "\n",
+    "    # Track gradient norm\n",
+    "    for layer, gradient in model.get_gradients().items():\n",
+    "        grad_norms[f\"grad_norm/{name}\"] = gradient.norm().item()\n",
+    "\n",
     "    # Output loss for this epoch\n",
     "    run.log_metrics(\n",
     "        data = {\n",
     "            \"training/loss\": loss.item(),\n",
     "            \"validation/loss\": val_loss,\n",
     "            **activation_dict_mean,\n",
-    "            **activation_dict_std\n",
+    "            **activation_dict_std,\n",
+    "            **grad_norms\n",
     "        },\n",
     "        step = epoch\n",
     "    )\n",
@@ -449,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {

From d4d160c5f4dae47a760ea04dbbf3c18317b295fd Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 16:18:07 +0100
Subject: [PATCH 019/125] refactor: add gradient tracking per epoch

---
 .../pytorch_text_model_debugging.ipynb        | 25 ++++++++++++++-----
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 400e331..754c309 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -22,8 +22,8 @@
    "outputs": [],
    "source": [
     "# TODO - improve dataset to be more realistic\n",
-    "# TODO - capture the folowings; attentions, gradients - weights, loss (agg and per layer), learning rate (per layer if needed)\n",
-    "    # complete - activations, grad norms\n",
+    "# TODO - capture the folowings; attentions, , loss (per layer), learning rate (per layer if needed)\n",
+    "    # complete - activations, grad norms, gradients - weights+biases, loss - agg\n",
     "# TODO - capture per layer losses\n",
     "# TODO - increase number of layers\n",
     "# TODO - Adding dropout to demonstrate the effects of regularization\n",
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -404,6 +404,9 @@
     "activation_dict_mean = {}\n",
     "activation_dict_std = {}\n",
     "grad_norms = {}\n",
+    "gradients_mean = {}\n",
+    "gradients_std = {}\n",
+    "gradient_norms = {}\n",
     "\n",
     "# Training loop\n",
     "for epoch in range(params[\"epochs\"]):\n",
@@ -439,6 +442,13 @@
     "    for layer, gradient in model.get_gradients().items():\n",
     "        grad_norms[f\"grad_norm/{name}\"] = gradient.norm().item()\n",
     "\n",
+    "    # Track gradients per layer at each epoch\n",
+    "    for name, param in model.named_parameters():\n",
+    "        if param is not None:\n",
+    "            gradients_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
+    "            gradients_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
+    "            gradient_norms[f\"layers/layer_{name}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
+    "\n",
     "    # Output loss for this epoch\n",
     "    run.log_metrics(\n",
     "        data = {\n",
@@ -446,7 +456,10 @@
     "            \"validation/loss\": val_loss,\n",
     "            **activation_dict_mean,\n",
     "            **activation_dict_std,\n",
-    "            **grad_norms\n",
+    "            **grad_norms,\n",
+    "            **gradients_std,\n",
+    "            **gradients_mean,\n",
+    "            **gradient_norms\n",
     "        },\n",
     "        step = epoch\n",
     "    )\n",
@@ -458,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {

From 890bf2652ff5714dc6df774ea475f06505d00b1f Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 3 Mar 2025 16:26:01 +0100
Subject: [PATCH 020/125] chore: remove uneeded section

---
 .../pytorch_text_model_debugging.ipynb        | 20 +------------------
 1 file changed, 1 insertion(+), 19 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 754c309..7683c79 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -22,7 +22,7 @@
    "outputs": [],
    "source": [
     "# TODO - improve dataset to be more realistic\n",
-    "# TODO - capture the folowings; attentions, , loss (per layer), learning rate (per layer if needed)\n",
+    "# TODO - capture the folowings; attentions, learning rate (per layer if needed)\n",
     "    # complete - activations, grad norms, gradients - weights+biases, loss - agg\n",
     "# TODO - capture per layer losses\n",
     "# TODO - increase number of layers\n",
@@ -228,24 +228,6 @@
     "    return hook"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = {\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 256,\n",
-    "    \"learning_rate\": 0.01,\n",
-    "    \"epochs\": 5, \n",
-    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
-    "    \"input_features\": 256,\n",
-    "    \"n_classes\": 10,\n",
-    "    \"input_size\": 28 * 28\n",
-    "}"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From ebd34c7f55854dd80fe103bfdc4328b17e14ac71 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 4 Mar 2025 10:44:51 +0100
Subject: [PATCH 021/125] refactor: add fully connected layer to model for more
 complexity

---
 .../pytorch_text_model_debugging.ipynb        | 245 ++++++++++--------
 1 file changed, 144 insertions(+), 101 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 7683c79..adf4411 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
     "    \"input_features\": 256,\n",
     "    \"n_classes\": 10, # TODO - remove\n",
     "    \"input_size\": 28 * 28, # TODO - remove\n",
-    "    \"vocab_size\": 200000,\n",
+    "    \"vocab_size\": 300000,\n",
     "    \"embed_size\": 1000,\n",
     "    \"hidden_size\": 256 # hidden size for the LSTM\n",
     "}"
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,8 +157,9 @@
     "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
     "        super(SimpleLLM, self).__init__()\n",
     "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, hidden_size, batch_first=True)\n",
-    "        self.fc = nn.Linear(hidden_size, vocab_size)\n",
+    "        self.lstm = nn.LSTM(embed_size, 512, batch_first=True)\n",
+    "        self.fc1 = nn.Linear(512, hidden_size)\n",
+    "        self.fc2 = nn.Linear(hidden_size, vocab_size)\n",
     "\n",
     "        # Initialize activations dictionary to store layer activations\n",
     "        self.activations = {}\n",
@@ -166,16 +167,19 @@
     "        ## Registering hooks to track activations\n",
     "        self.hooks = []\n",
     "        self.hooks.append(self.lstm.register_forward_hook(self.save_activation(\"lstm\")))\n",
-    "        self.hooks.append(self.fc.register_forward_hook(self.save_activation(\"fc\")))\n",
+    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
+    "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
     "\n",
     "        ## Registering hooks to track gradients\n",
     "        self.hooks.append(self.lstm.register_full_backward_hook(self.save_gradient(\"lstm\")))\n",
-    "        self.hooks.append(self.fc.register_full_backward_hook(self.save_gradient(\"fc\")))\n",
+    "        self.hooks.append(self.fc1.register_full_backward_hook(self.save_gradient(\"fc1\")))\n",
+    "        self.hooks.append(self.fc2.register_full_backward_hook(self.save_gradient(\"fc2\")))\n",
     "        \n",
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
     "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
-    "        out = self.fc(lstm_out)\n",
+    "        out = self.fc1(lstm_out)\n",
+    "        out = self.fc2(out)\n",
     "        return out\n",
     "    \n",
     "    # Function to save activations\n",
@@ -249,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,92 +280,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/400, Loss: 11.5970\n",
-      "Epoch 2/400, Loss: 11.0686\n",
-      "Epoch 3/400, Loss: 10.5915\n",
-      "Epoch 4/400, Loss: 10.1422\n",
-      "Epoch 5/400, Loss: 9.7076\n",
-      "Epoch 6/400, Loss: 9.2819\n",
-      "Epoch 7/400, Loss: 8.8626\n",
-      "Epoch 8/400, Loss: 8.4479\n",
-      "Epoch 9/400, Loss: 8.0365\n",
-      "Epoch 10/400, Loss: 7.6276\n",
-      "Epoch 11/400, Loss: 7.2211\n",
-      "Epoch 12/400, Loss: 6.8171\n",
-      "Epoch 13/400, Loss: 6.4153\n",
-      "Epoch 14/400, Loss: 6.0154\n",
-      "Epoch 15/400, Loss: 5.6169\n",
-      "Epoch 16/400, Loss: 5.2196\n",
-      "Epoch 17/400, Loss: 4.8243\n",
-      "Epoch 18/400, Loss: 4.4318\n",
-      "Epoch 19/400, Loss: 4.0431\n",
-      "Epoch 20/400, Loss: 3.6596\n",
-      "Epoch 21/400, Loss: 3.2834\n",
-      "Epoch 22/400, Loss: 2.9174\n",
-      "Epoch 23/400, Loss: 2.5654\n",
-      "Epoch 24/400, Loss: 2.2321\n",
-      "Epoch 25/400, Loss: 1.9223\n",
-      "Epoch 26/400, Loss: 1.6402\n",
-      "Epoch 27/400, Loss: 1.3889\n",
-      "Epoch 28/400, Loss: 1.1696\n",
-      "Epoch 29/400, Loss: 0.9814\n",
-      "Epoch 30/400, Loss: 0.8221\n",
-      "Epoch 31/400, Loss: 0.6882\n",
-      "Epoch 32/400, Loss: 0.5760\n",
-      "Epoch 33/400, Loss: 0.4818\n",
-      "Epoch 34/400, Loss: 0.4025\n",
-      "Epoch 35/400, Loss: 0.3356\n",
-      "Epoch 36/400, Loss: 0.2791\n",
-      "Epoch 37/400, Loss: 0.2315\n",
-      "Epoch 38/400, Loss: 0.1917\n",
-      "Epoch 39/400, Loss: 0.1585\n",
-      "Epoch 40/400, Loss: 0.1311\n",
-      "Epoch 41/400, Loss: 0.1086\n",
-      "Epoch 42/400, Loss: 0.0903\n",
-      "Epoch 43/400, Loss: 0.0755\n",
-      "Epoch 44/400, Loss: 0.0634\n",
-      "Epoch 45/400, Loss: 0.0537\n",
-      "Epoch 46/400, Loss: 0.0457\n",
-      "Epoch 47/400, Loss: 0.0393\n",
-      "Epoch 48/400, Loss: 0.0340\n",
-      "Epoch 49/400, Loss: 0.0297\n",
-      "Epoch 50/400, Loss: 0.0262\n",
-      "Epoch 51/400, Loss: 0.0233\n",
-      "Epoch 52/400, Loss: 0.0209\n",
-      "Epoch 53/400, Loss: 0.0189\n",
-      "Epoch 54/400, Loss: 0.0172\n",
-      "Epoch 55/400, Loss: 0.0158\n",
-      "Epoch 56/400, Loss: 0.0146\n",
-      "Epoch 57/400, Loss: 0.0136\n",
-      "Epoch 58/400, Loss: 0.0128\n",
-      "Epoch 59/400, Loss: 0.0120\n",
-      "Epoch 60/400, Loss: 0.0114\n",
-      "Epoch 61/400, Loss: 0.0108\n",
-      "Epoch 62/400, Loss: 0.0103\n",
-      "Epoch 63/400, Loss: 0.0099\n",
-      "Epoch 64/400, Loss: 0.0095\n",
-      "Epoch 65/400, Loss: 0.0091\n",
-      "Epoch 66/400, Loss: 0.0088\n",
-      "Epoch 67/400, Loss: 0.0086\n",
-      "Epoch 68/400, Loss: 0.0083\n",
-      "Epoch 69/400, Loss: 0.0081\n",
-      "Epoch 70/400, Loss: 0.0079\n",
-      "Epoch 71/400, Loss: 0.0077\n",
-      "Epoch 72/400, Loss: 0.0075\n",
-      "Epoch 73/400, Loss: 0.0074\n",
-      "Epoch 74/400, Loss: 0.0072\n",
-      "Epoch 75/400, Loss: 0.0071\n",
-      "Epoch 76/400, Loss: 0.0070\n",
-      "Epoch 77/400, Loss: 0.0068\n",
-      "Epoch 78/400, Loss: 0.0067\n",
-      "Epoch 79/400, Loss: 0.0066\n"
+      "Epoch 1/400, Loss: 12.6022\n",
+      "Epoch 2/400, Loss: 11.9868\n",
+      "Epoch 3/400, Loss: 11.3907\n",
+      "Epoch 4/400, Loss: 10.7466\n",
+      "Epoch 5/400, Loss: 10.0276\n",
+      "Epoch 6/400, Loss: 9.2259\n",
+      "Epoch 7/400, Loss: 8.3386\n",
+      "Epoch 8/400, Loss: 7.3620\n",
+      "Epoch 9/400, Loss: 6.2913\n",
+      "Epoch 10/400, Loss: 5.1245\n",
+      "Epoch 11/400, Loss: 3.8811\n",
+      "Epoch 12/400, Loss: 2.6654\n",
+      "Epoch 13/400, Loss: 1.6767\n",
+      "Epoch 14/400, Loss: 1.0357\n",
+      "Epoch 15/400, Loss: 0.6587\n",
+      "Epoch 16/400, Loss: 0.3819\n",
+      "Epoch 17/400, Loss: 0.1724\n",
+      "Epoch 18/400, Loss: 0.0595\n",
+      "Epoch 19/400, Loss: 0.0203\n",
+      "Epoch 20/400, Loss: 0.0097\n",
+      "Epoch 21/400, Loss: 0.0066\n",
+      "Epoch 22/400, Loss: 0.0053\n",
+      "Epoch 23/400, Loss: 0.0046\n",
+      "Epoch 24/400, Loss: 0.0041\n",
+      "Epoch 25/400, Loss: 0.0037\n",
+      "Epoch 26/400, Loss: 0.0033\n",
+      "Epoch 27/400, Loss: 0.0030\n",
+      "Epoch 28/400, Loss: 0.0027\n",
+      "Epoch 29/400, Loss: 0.0024\n",
+      "Epoch 30/400, Loss: 0.0022\n",
+      "Epoch 31/400, Loss: 0.0020\n",
+      "Epoch 32/400, Loss: 0.0018\n",
+      "Epoch 33/400, Loss: 0.0017\n",
+      "Epoch 34/400, Loss: 0.0015\n",
+      "Epoch 35/400, Loss: 0.0014\n",
+      "Epoch 36/400, Loss: 0.0013\n",
+      "Epoch 37/400, Loss: 0.0012\n",
+      "Epoch 38/400, Loss: 0.0012\n",
+      "Epoch 39/400, Loss: 0.0011\n",
+      "Epoch 40/400, Loss: 0.0010\n",
+      "Epoch 41/400, Loss: 0.0010\n",
+      "Epoch 42/400, Loss: 0.0009\n",
+      "Epoch 43/400, Loss: 0.0009\n",
+      "Epoch 44/400, Loss: 0.0008\n",
+      "Epoch 45/400, Loss: 0.0008\n",
+      "Epoch 46/400, Loss: 0.0008\n",
+      "Epoch 47/400, Loss: 0.0007\n",
+      "Epoch 48/400, Loss: 0.0007\n",
+      "Epoch 49/400, Loss: 0.0007\n",
+      "Epoch 50/400, Loss: 0.0007\n",
+      "Epoch 51/400, Loss: 0.0006\n",
+      "Epoch 52/400, Loss: 0.0006\n",
+      "Epoch 53/400, Loss: 0.0006\n",
+      "Epoch 54/400, Loss: 0.0006\n",
+      "Epoch 55/400, Loss: 0.0006\n",
+      "Epoch 56/400, Loss: 0.0005\n",
+      "Epoch 57/400, Loss: 0.0005\n",
+      "Epoch 58/400, Loss: 0.0005\n",
+      "Epoch 59/400, Loss: 0.0005\n",
+      "Epoch 60/400, Loss: 0.0005\n",
+      "Epoch 61/400, Loss: 0.0005\n",
+      "Epoch 62/400, Loss: 0.0005\n",
+      "Epoch 63/400, Loss: 0.0005\n",
+      "Epoch 64/400, Loss: 0.0005\n",
+      "Epoch 65/400, Loss: 0.0005\n",
+      "Epoch 66/400, Loss: 0.0004\n",
+      "Epoch 67/400, Loss: 0.0004\n",
+      "Epoch 68/400, Loss: 0.0004\n",
+      "Epoch 69/400, Loss: 0.0004\n",
+      "Epoch 70/400, Loss: 0.0004\n",
+      "Epoch 71/400, Loss: 0.0004\n",
+      "Epoch 72/400, Loss: 0.0004\n",
+      "Epoch 73/400, Loss: 0.0004\n",
+      "Epoch 74/400, Loss: 0.0004\n",
+      "Epoch 75/400, Loss: 0.0004\n",
+      "Epoch 76/400, Loss: 0.0004\n",
+      "Epoch 77/400, Loss: 0.0004\n",
+      "Epoch 78/400, Loss: 0.0004\n",
+      "Epoch 79/400, Loss: 0.0004\n",
+      "Epoch 80/400, Loss: 0.0004\n",
+      "Epoch 81/400, Loss: 0.0004\n",
+      "Epoch 82/400, Loss: 0.0004\n",
+      "Epoch 83/400, Loss: 0.0004\n",
+      "Epoch 84/400, Loss: 0.0004\n",
+      "Epoch 85/400, Loss: 0.0004\n",
+      "Epoch 86/400, Loss: 0.0003\n",
+      "Epoch 87/400, Loss: 0.0003\n",
+      "Epoch 88/400, Loss: 0.0003\n",
+      "Epoch 89/400, Loss: 0.0003\n",
+      "Epoch 90/400, Loss: 0.0003\n",
+      "Epoch 91/400, Loss: 0.0003\n",
+      "Epoch 92/400, Loss: 0.0003\n",
+      "Epoch 93/400, Loss: 0.0003\n",
+      "Epoch 94/400, Loss: 0.0003\n",
+      "Epoch 95/400, Loss: 0.0003\n",
+      "Epoch 96/400, Loss: 0.0003\n",
+      "Epoch 97/400, Loss: 0.0003\n",
+      "Epoch 98/400, Loss: 0.0003\n",
+      "Epoch 99/400, Loss: 0.0003\n",
+      "Epoch 100/400, Loss: 0.0003\n",
+      "Epoch 101/400, Loss: 0.0003\n",
+      "Epoch 102/400, Loss: 0.0003\n",
+      "Epoch 103/400, Loss: 0.0003\n",
+      "Epoch 104/400, Loss: 0.0003\n",
+      "Epoch 105/400, Loss: 0.0003\n",
+      "Epoch 106/400, Loss: 0.0003\n",
+      "Epoch 107/400, Loss: 0.0003\n",
+      "Epoch 108/400, Loss: 0.0003\n",
+      "Epoch 109/400, Loss: 0.0003\n",
+      "Epoch 110/400, Loss: 0.0003\n",
+      "Epoch 111/400, Loss: 0.0003\n",
+      "Epoch 112/400, Loss: 0.0003\n",
+      "Epoch 113/400, Loss: 0.0003\n",
+      "Epoch 114/400, Loss: 0.0003\n",
+      "Epoch 115/400, Loss: 0.0003\n",
+      "Epoch 116/400, Loss: 0.0003\n",
+      "Epoch 117/400, Loss: 0.0003\n",
+      "Epoch 118/400, Loss: 0.0003\n",
+      "Epoch 119/400, Loss: 0.0003\n",
+      "Epoch 120/400, Loss: 0.0003\n",
+      "Epoch 121/400, Loss: 0.0003\n",
+      "Epoch 122/400, Loss: 0.0003\n",
+      "Epoch 123/400, Loss: 0.0003\n"
      ]
     },
     {
@@ -371,13 +419,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[36], line 26\u001b[0m\n\u001b[0;32m     23\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     25\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[1;32m---> 26\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     28\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m evaluate_model(model, val_input, val_target, params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvocab_size\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:476\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    474\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (max_exp_avg_sqs[i]\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 476\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (\u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m    478\u001b[0m     param\u001b[38;5;241m.\u001b[39maddcdiv_(exp_avg, denom, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39mstep_size)\n\u001b[0;32m    480\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[126], line 45\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, param \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mnamed_parameters():\n\u001b[0;32m     44\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m param \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 45\u001b[0m         gradients_std[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayers/layer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_std\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mparam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     46\u001b[0m         gradients_mean[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayers/layer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_mean\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m param\u001b[38;5;241m.\u001b[39mgrad\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     47\u001b[0m         gradient_norms[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayers/layer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_norm\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m param\u001b[38;5;241m.\u001b[39mgrad\u001b[38;5;241m.\u001b[39mnorm()\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;66;03m# L2 norm (Euclidean norm) of the gradients\u001b[39;00m\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -434,8 +476,9 @@
     "    # Output loss for this epoch\n",
     "    run.log_metrics(\n",
     "        data = {\n",
-    "            \"training/loss\": loss.item(),\n",
-    "            \"validation/loss\": val_loss,\n",
+    "            \"metrics/train/loss\": loss.item(),\n",
+    "            \"metrics/validation/loss\": val_loss,\n",
+    "            \"epoch/value\": epoch,\n",
     "            **activation_dict_mean,\n",
     "            **activation_dict_std,\n",
     "            **grad_norms,\n",
@@ -453,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {

From ae9abc67673914961c3e9d7c12a7d1fad8a71bf0 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 4 Mar 2025 14:13:10 +0100
Subject: [PATCH 022/125] chore: fix activation saving for layers

---
 .../pytorch_image_classification.ipynb        | 64 ++++++++-----------
 1 file changed, 26 insertions(+), 38 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index c36f7bd..0fd0e1c 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Neptune + PyTorch\n",
+    "# Neptune + Pytorch\n",
+    "## Logging and visualizing debugging metrics in PyTorch\n",
     "\n",
     "Introduction\n",
     "\n",
@@ -15,7 +16,7 @@
     "- Log standard loss and accuracy metrics to Neptune\n",
     "- Log debugging metrics during model training such as;\n",
     "    * Activations per layer\n",
-    "    * Gradients (mean and std weights and biases) per layer"
+    "    * Gradients (mean and std) per layer"
    ]
   },
   {
@@ -60,12 +61,13 @@
     "# TODO - clean up the evaluation function to exclude tracking gradients\n",
     "# TODO - do not use group tags\n",
     "# TODO - track the input features\n",
-    "# TODO - clean the training loop of commented out code that is unused"
+    "# TODO - clean the training loop of commented out code that is unused\n",
+    "# TODO - add batchnormalization and drop out layers to improve the model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -184,8 +186,8 @@
     "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
     "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
     "        self.hooks.append(self.fc3.register_forward_hook(self.save_activation(\"fc3\")))\n",
-    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc4\")))\n",
-    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc5\")))\n",
+    "        self.hooks.append(self.fc4.register_forward_hook(self.save_activation(\"fc4\")))\n",
+    "        self.hooks.append(self.fc5.register_forward_hook(self.save_activation(\"fc5\")))\n",
     "\n",
     "    def forward(self, x):\n",
     "        x = x.view(-1, params[\"input_size\"])  # Flatten the input image (28x28)\n",
@@ -226,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,38 +303,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch [1/5] Training complete. Loss: 0.9066, Accuracy: 0.69%\n",
-      "{'layers/layer_fc1/activation_mean': -27.44853973388672, 'layers/layer_fc4/activation_mean': -27.44853973388672, 'layers/layer_fc5/activation_mean': -27.44853973388672, 'layers/layer_fc2/activation_mean': -3.266420602798462, 'layers/layer_fc3/activation_mean': -3.977348804473877, 'layers/layer_fc1/activation_std': 9.884493827819824, 'layers/layer_fc4/activation_std': 9.884493827819824, 'layers/layer_fc5/activation_std': 9.884493827819824, 'layers/layer_fc2/activation_std': 2.8974952697753906, 'layers/layer_fc3/activation_std': 3.9147353172302246, 'layers/layer_fc1.weight_mean': 1.759609585860744e-05, 'layers/layer_fc1.bias_mean': -2.6617166440701112e-05, 'layers/layer_fc2.weight_mean': 1.845465158112347e-05, 'layers/layer_fc2.bias_mean': 6.048093564459123e-05, 'layers/layer_fc3.weight_mean': 6.554154424520675e-06, 'layers/layer_fc3.bias_mean': 4.6133769501466304e-05, 'layers/layer_fc4.weight_mean': -1.4562309843313415e-05, 'layers/layer_fc4.bias_mean': 1.2838396287406795e-05, 'layers/layer_fc5.weight_mean': 4.2628095497931895e-10, 'layers/layer_fc5.bias_mean': -9.313225884932663e-11, 'layers/layer_fc1.weight_std': 0.0014361083740368485, 'layers/layer_fc1.bias_std': 0.0014619147405028343, 'layers/layer_fc2.weight_std': 0.001974125625565648, 'layers/layer_fc2.bias_std': 0.0011026636930182576, 'layers/layer_fc3.weight_std': 0.0008759713382460177, 'layers/layer_fc3.bias_std': 0.0011844916734844446, 'layers/layer_fc4.weight_std': 0.002118356991559267, 'layers/layer_fc4.bias_std': 0.00178423966281116, 'layers/layer_fc5.weight_std': 0.021144278347492218, 'layers/layer_fc5.bias_std': 0.016225792467594147, 'grad_norm/fc1.weight': 0.6434243321418762, 'grad_norm/fc1.bias': 0.023348789662122726, 'grad_norm/fc2.weight': 0.714738130569458, 'grad_norm/fc2.bias': 0.02496359497308731, 'grad_norm/fc3.weight': 0.31714311242103577, 'grad_norm/fc3.bias': 0.018929213285446167, 'grad_norm/fc4.weight': 0.38346678018569946, 'grad_norm/fc4.bias': 0.02010788396000862, 'grad_norm/fc5.weight': 0.7561851143836975, 'grad_norm/fc5.bias': 0.04867737740278244}\n",
-      "Epoch [2/5] Training complete. Loss: 0.2738, Accuracy: 0.92%\n",
-      "{'layers/layer_fc1/activation_mean': -27.444847106933594, 'layers/layer_fc4/activation_mean': -27.444847106933594, 'layers/layer_fc5/activation_mean': -27.444847106933594, 'layers/layer_fc2/activation_mean': -4.043460845947266, 'layers/layer_fc3/activation_mean': -4.557342529296875, 'layers/layer_fc1/activation_std': 9.927286148071289, 'layers/layer_fc4/activation_std': 9.927286148071289, 'layers/layer_fc5/activation_std': 9.927286148071289, 'layers/layer_fc2/activation_std': 3.500694990158081, 'layers/layer_fc3/activation_std': 4.5128021240234375, 'layers/layer_fc1.weight_mean': 3.658090008684667e-07, 'layers/layer_fc1.bias_mean': 3.3925662137335166e-06, 'layers/layer_fc2.weight_mean': -2.423007288143708e-07, 'layers/layer_fc2.bias_mean': 1.9860681277350523e-05, 'layers/layer_fc3.weight_mean': 5.834312105434947e-06, 'layers/layer_fc3.bias_mean': -6.391452188836411e-06, 'layers/layer_fc4.weight_mean': 7.309272405109368e-06, 'layers/layer_fc4.bias_mean': 3.118724634987302e-06, 'layers/layer_fc5.weight_mean': 3.8910458188823327e-10, 'layers/layer_fc5.bias_mean': 1.3038515822572094e-09, 'layers/layer_fc1.weight_std': 0.0011528775794431567, 'layers/layer_fc1.bias_std': 0.0012104393681511283, 'layers/layer_fc2.weight_std': 0.0022255387157201767, 'layers/layer_fc2.bias_std': 0.0010018462780863047, 'layers/layer_fc3.weight_std': 0.0007322281016968191, 'layers/layer_fc3.bias_std': 0.0008695347351022065, 'layers/layer_fc4.weight_std': 0.0019773344974964857, 'layers/layer_fc4.bias_std': 0.0012858008267357945, 'layers/layer_fc5.weight_std': 0.015919363126158714, 'layers/layer_fc5.bias_std': 0.011463840492069721, 'grad_norm/fc1.weight': 0.5164875984191895, 'grad_norm/fc1.bias': 0.019329242408275604, 'grad_norm/fc2.weight': 0.8057278990745544, 'grad_norm/fc2.bias': 0.02265150472521782, 'grad_norm/fc3.weight': 0.26510223746299744, 'grad_norm/fc3.bias': 0.013885731808841228, 'grad_norm/fc4.weight': 0.3579327464103699, 'grad_norm/fc4.bias': 0.014490283094346523, 'grad_norm/fc5.weight': 0.5693259239196777, 'grad_norm/fc5.bias': 0.03439152240753174}\n",
-      "Epoch [3/5] Training complete. Loss: 0.2052, Accuracy: 0.94%\n",
-      "{'layers/layer_fc1/activation_mean': -27.409822463989258, 'layers/layer_fc4/activation_mean': -27.409822463989258, 'layers/layer_fc5/activation_mean': -27.409822463989258, 'layers/layer_fc2/activation_mean': -4.827888011932373, 'layers/layer_fc3/activation_mean': -5.646310329437256, 'layers/layer_fc1/activation_std': 10.087625503540039, 'layers/layer_fc4/activation_std': 10.087625503540039, 'layers/layer_fc5/activation_std': 10.087625503540039, 'layers/layer_fc2/activation_std': 4.256272792816162, 'layers/layer_fc3/activation_std': 5.8036675453186035, 'layers/layer_fc1.weight_mean': -1.7095830116886646e-05, 'layers/layer_fc1.bias_mean': 2.0519555619102903e-05, 'layers/layer_fc2.weight_mean': -1.0371921234764159e-05, 'layers/layer_fc2.bias_mean': -2.6764431822812185e-05, 'layers/layer_fc3.weight_mean': 1.1725012427632464e-07, 'layers/layer_fc3.bias_mean': -3.2187872420763597e-06, 'layers/layer_fc4.weight_mean': -4.269544206181308e-06, 'layers/layer_fc4.bias_mean': 2.4562015823903494e-05, 'layers/layer_fc5.weight_mean': 5.096634225765229e-10, 'layers/layer_fc5.bias_mean': 1.3038515822572094e-09, 'layers/layer_fc1.weight_std': 0.0010182075202465057, 'layers/layer_fc1.bias_std': 0.0011107329046353698, 'layers/layer_fc2.weight_std': 0.0014369278214871883, 'layers/layer_fc2.bias_std': 0.0006925289635546505, 'layers/layer_fc3.weight_std': 0.0004932511947117746, 'layers/layer_fc3.bias_std': 0.0006599067128263414, 'layers/layer_fc4.weight_std': 0.0011504496214911342, 'layers/layer_fc4.bias_std': 0.001063313102349639, 'layers/layer_fc5.weight_std': 0.010835869237780571, 'layers/layer_fc5.bias_std': 0.010039512068033218, 'grad_norm/fc1.weight': 0.4562200903892517, 'grad_norm/fc1.bias': 0.017740018665790558, 'grad_norm/fc2.weight': 0.520235002040863, 'grad_norm/fc2.bias': 0.01566654071211815, 'grad_norm/fc3.weight': 0.1785753071308136, 'grad_norm/fc3.bias': 0.010537991300225258, 'grad_norm/fc4.weight': 0.20825187861919403, 'grad_norm/fc4.bias': 0.011986151337623596, 'grad_norm/fc5.weight': 0.3875243663787842, 'grad_norm/fc5.bias': 0.030118538066744804}\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[94], line 47\u001b[0m\n\u001b[0;32m     43\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m correct_preds \u001b[38;5;241m/\u001b[39m total_preds\n\u001b[0;32m     44\u001b[0m \u001b[38;5;66;03m# print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     45\u001b[0m         \n\u001b[0;32m     46\u001b[0m \u001b[38;5;66;03m# Validation step per training step\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m val_loss, val_accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Evaluate after each step\u001b[39;00m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;66;03m# print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, param \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mnamed_parameters():\n",
-      "Cell \u001b[1;32mIn[80], line 8\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, data_loader, track_gradients)\u001b[0m\n\u001b[0;32m      6\u001b[0m epoch_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():  \u001b[38;5;66;03m# Disable gradient tracking during evaluation\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# Forward pass (with gradient tracking if specified)\u001b[39;49;00m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Correct loss computation\u001b[39;49;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\datasets\\mnist.py:146\u001b[0m, in \u001b[0;36mMNIST.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    143\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img\u001b[38;5;241m.\u001b[39mnumpy(), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 146\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    149\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:137\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[1;34m(self, pic)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pic):\n\u001b[0;32m    130\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    131\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    132\u001b[0m \u001b[38;5;124;03m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;124;03m        Tensor: Converted image.\u001b[39;00m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpic\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\functional.py:176\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m    174\u001b[0m img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(img, torch\u001b[38;5;241m.\u001b[39mByteTensor):\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_float_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdiv(\u001b[38;5;241m255\u001b[39m)\n\u001b[0;32m    177\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    178\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "Epoch [1/5] Training complete. Loss: 0.5882, Accuracy: 0.80%\n",
+      "{'layers/layer_fc1/activation_mean': -28.098386764526367, 'layers/layer_fc4/activation_mean': -28.098386764526367, 'layers/layer_fc5/activation_mean': -28.098386764526367, 'layers/layer_fc2/activation_mean': -4.294741630554199, 'layers/layer_fc3/activation_mean': -4.590151309967041, 'layers/layer_fc1/activation_std': 10.138233184814453, 'layers/layer_fc4/activation_std': 10.138233184814453, 'layers/layer_fc5/activation_std': 10.138233184814453, 'layers/layer_fc2/activation_std': 3.513108730316162, 'layers/layer_fc3/activation_std': 4.694570064544678, 'layers/layer_fc1.weight_mean': -4.2270323319826275e-05, 'layers/layer_fc1.bias_mean': 5.756489190389402e-05, 'layers/layer_fc2.weight_mean': 1.7486901924712583e-05, 'layers/layer_fc2.bias_mean': 5.119257184560411e-05, 'layers/layer_fc3.weight_mean': -6.444352038670331e-06, 'layers/layer_fc3.bias_mean': -3.2650757930241525e-05, 'layers/layer_fc4.weight_mean': 4.735463880933821e-05, 'layers/layer_fc4.bias_mean': 0.00019672312191687524, 'layers/layer_fc5.weight_mean': 2.773958840407431e-11, 'layers/layer_fc5.bias_mean': -3.4924596548080444e-10, 'layers/layer_fc1.weight_std': 0.0018884788732975721, 'layers/layer_fc1.bias_std': 0.0020714548882097006, 'layers/layer_fc2.weight_std': 0.002078161109238863, 'layers/layer_fc2.bias_std': 0.0017285202629864216, 'layers/layer_fc3.weight_std': 0.0006235535256564617, 'layers/layer_fc3.bias_std': 0.0014959467807784677, 'layers/layer_fc4.weight_std': 0.001397876301780343, 'layers/layer_fc4.bias_std': 0.001897347392514348, 'layers/layer_fc5.weight_std': 0.014698885381221771, 'layers/layer_fc5.bias_std': 0.015608142130076885, 'grad_norm/fc1.weight': 0.8462492823600769, 'grad_norm/fc1.bias': 0.03309129923582077, 'grad_norm/fc2.weight': 0.7523983716964722, 'grad_norm/fc2.bias': 0.039090901613235474, 'grad_norm/fc3.weight': 0.22576160728931427, 'grad_norm/fc3.bias': 0.023894065991044044, 'grad_norm/fc4.weight': 0.25318393111228943, 'grad_norm/fc4.bias': 0.021497542038559914, 'grad_norm/fc5.weight': 0.5256778597831726, 'grad_norm/fc5.bias': 0.04682442545890808}\n",
+      "Epoch [2/5] Training complete. Loss: 0.2444, Accuracy: 0.93%\n",
+      "{'layers/layer_fc1/activation_mean': -28.045276641845703, 'layers/layer_fc4/activation_mean': -28.045276641845703, 'layers/layer_fc5/activation_mean': -28.045276641845703, 'layers/layer_fc2/activation_mean': -6.288389682769775, 'layers/layer_fc3/activation_mean': -7.880246162414551, 'layers/layer_fc1/activation_std': 10.427655220031738, 'layers/layer_fc4/activation_std': 10.427655220031738, 'layers/layer_fc5/activation_std': 10.427655220031738, 'layers/layer_fc2/activation_std': 5.016049385070801, 'layers/layer_fc3/activation_std': 7.8910441398620605, 'layers/layer_fc1.weight_mean': -1.8336819266551174e-05, 'layers/layer_fc1.bias_mean': 2.4514503820682876e-05, 'layers/layer_fc2.weight_mean': -4.397415978019126e-06, 'layers/layer_fc2.bias_mean': -5.701838745153509e-06, 'layers/layer_fc3.weight_mean': 3.609377472457709e-06, 'layers/layer_fc3.bias_mean': 2.2656186047242954e-05, 'layers/layer_fc4.weight_mean': 3.2792174806672847e-06, 'layers/layer_fc4.bias_mean': 6.183851655805483e-05, 'layers/layer_fc5.weight_mean': 1.033322427623773e-09, 'layers/layer_fc5.bias_mean': 2.793967834868738e-10, 'layers/layer_fc1.weight_std': 0.0009652073495090008, 'layers/layer_fc1.bias_std': 0.0011003392282873392, 'layers/layer_fc2.weight_std': 0.0015912855742499232, 'layers/layer_fc2.bias_std': 0.0008093378273770213, 'layers/layer_fc3.weight_std': 0.000562622444704175, 'layers/layer_fc3.bias_std': 0.0008122650324366987, 'layers/layer_fc4.weight_std': 0.0013753673993051052, 'layers/layer_fc4.bias_std': 0.001243805163539946, 'layers/layer_fc5.weight_std': 0.02044137567281723, 'layers/layer_fc5.bias_std': 0.01635204255580902, 'grad_norm/fc1.weight': 0.4324897825717926, 'grad_norm/fc1.bias': 0.01757538504898548, 'grad_norm/fc2.weight': 0.57610684633255, 'grad_norm/fc2.bias': 0.018295787274837494, 'grad_norm/fc3.weight': 0.2036944478750229, 'grad_norm/fc3.bias': 0.012975896708667278, 'grad_norm/fc4.weight': 0.24896498024463654, 'grad_norm/fc4.bias': 0.014034420251846313, 'grad_norm/fc5.weight': 0.7310471534729004, 'grad_norm/fc5.bias': 0.04905613139271736}\n",
+      "Epoch [3/5] Training complete. Loss: 0.2124, Accuracy: 0.94%\n",
+      "{'layers/layer_fc1/activation_mean': -28.00644874572754, 'layers/layer_fc4/activation_mean': -28.00644874572754, 'layers/layer_fc5/activation_mean': -28.00644874572754, 'layers/layer_fc2/activation_mean': -8.306803703308105, 'layers/layer_fc3/activation_mean': -10.63320255279541, 'layers/layer_fc1/activation_std': 10.678731918334961, 'layers/layer_fc4/activation_std': 10.678731918334961, 'layers/layer_fc5/activation_std': 10.678731918334961, 'layers/layer_fc2/activation_std': 6.999919891357422, 'layers/layer_fc3/activation_std': 10.323572158813477, 'layers/layer_fc1.weight_mean': 7.430891855619848e-05, 'layers/layer_fc1.bias_mean': -9.825517918216065e-05, 'layers/layer_fc2.weight_mean': 1.702215740806423e-05, 'layers/layer_fc2.bias_mean': 5.076182424090803e-05, 'layers/layer_fc3.weight_mean': 2.006397380682756e-06, 'layers/layer_fc3.bias_mean': 9.8647487902781e-06, 'layers/layer_fc4.weight_mean': 3.732190089067444e-05, 'layers/layer_fc4.bias_mean': 0.00015728663129266351, 'layers/layer_fc5.weight_mean': 5.190377017072478e-10, 'layers/layer_fc5.bias_mean': 7.450580707946131e-10, 'layers/layer_fc1.weight_std': 0.0013835423160344362, 'layers/layer_fc1.bias_std': 0.0014023504918441176, 'layers/layer_fc2.weight_std': 0.0018697652267292142, 'layers/layer_fc2.bias_std': 0.0008941664709709585, 'layers/layer_fc3.weight_std': 0.0007453133002854884, 'layers/layer_fc3.bias_std': 0.0008750202250666916, 'layers/layer_fc4.weight_std': 0.0016579279908910394, 'layers/layer_fc4.bias_std': 0.001322976779192686, 'layers/layer_fc5.weight_std': 0.018788360059261322, 'layers/layer_fc5.bias_std': 0.0166013166308403, 'grad_norm/fc1.weight': 0.620717465877533, 'grad_norm/fc1.bias': 0.02244885452091694, 'grad_norm/fc2.weight': 0.6769528388977051, 'grad_norm/fc2.bias': 0.020245518535375595, 'grad_norm/fc3.weight': 0.26983216404914856, 'grad_norm/fc3.bias': 0.013973843306303024, 'grad_norm/fc4.weight': 0.30018848180770874, 'grad_norm/fc4.bias': 0.015015012584626675, 'grad_norm/fc5.weight': 0.671930193901062, 'grad_norm/fc5.bias': 0.049803949892520905}\n",
+      "Epoch [4/5] Training complete. Loss: 0.1774, Accuracy: 0.95%\n",
+      "{'layers/layer_fc1/activation_mean': -28.02176284790039, 'layers/layer_fc4/activation_mean': -28.02176284790039, 'layers/layer_fc5/activation_mean': -28.02176284790039, 'layers/layer_fc2/activation_mean': -9.155248641967773, 'layers/layer_fc3/activation_mean': -13.8362455368042, 'layers/layer_fc1/activation_std': 10.731369972229004, 'layers/layer_fc4/activation_std': 10.731369972229004, 'layers/layer_fc5/activation_std': 10.731369972229004, 'layers/layer_fc2/activation_std': 7.8137617111206055, 'layers/layer_fc3/activation_std': 12.559845924377441, 'layers/layer_fc1.weight_mean': 4.3527179514057934e-05, 'layers/layer_fc1.bias_mean': -5.993247759761289e-05, 'layers/layer_fc2.weight_mean': -8.3655349953915e-06, 'layers/layer_fc2.bias_mean': -2.7970250812359154e-05, 'layers/layer_fc3.weight_mean': -6.794801265641581e-06, 'layers/layer_fc3.bias_mean': -5.020684693590738e-05, 'layers/layer_fc4.weight_mean': -5.56191080249846e-05, 'layers/layer_fc4.bias_mean': -0.00022704749426338822, 'layers/layer_fc5.weight_mean': -4.933287667263642e-10, 'layers/layer_fc5.bias_mean': -1.0710209386033398e-09, 'layers/layer_fc1.weight_std': 0.0006279172375798225, 'layers/layer_fc1.bias_std': 0.0006310796015895903, 'layers/layer_fc2.weight_std': 0.0009842520812526345, 'layers/layer_fc2.bias_std': 0.00036153788096271455, 'layers/layer_fc3.weight_std': 0.0003291342291049659, 'layers/layer_fc3.bias_std': 0.0003552739799488336, 'layers/layer_fc4.weight_std': 0.0006492410320788622, 'layers/layer_fc4.bias_std': 0.0005454771453514695, 'layers/layer_fc5.weight_std': 0.0076590655371546745, 'layers/layer_fc5.bias_std': 0.005646508652716875, 'grad_norm/fc1.weight': 0.28198111057281494, 'grad_norm/fc1.bias': 0.010123053565621376, 'grad_norm/fc2.weight': 0.35634881258010864, 'grad_norm/fc2.bias': 0.008197144605219364, 'grad_norm/fc3.weight': 0.119184210896492, 'grad_norm/fc3.bias': 0.005729861091822386, 'grad_norm/fc4.weight': 0.11795385181903839, 'grad_norm/fc4.bias': 0.006662336643785238, 'grad_norm/fc5.weight': 0.2739119827747345, 'grad_norm/fc5.bias': 0.016939526423811913}\n",
+      "Epoch [5/5] Training complete. Loss: 0.1836, Accuracy: 0.95%\n",
+      "{'layers/layer_fc1/activation_mean': -27.910594940185547, 'layers/layer_fc4/activation_mean': -27.910594940185547, 'layers/layer_fc5/activation_mean': -27.910594940185547, 'layers/layer_fc2/activation_mean': -11.856008529663086, 'layers/layer_fc3/activation_mean': -16.673297882080078, 'layers/layer_fc1/activation_std': 11.18954086303711, 'layers/layer_fc4/activation_std': 11.18954086303711, 'layers/layer_fc5/activation_std': 11.18954086303711, 'layers/layer_fc2/activation_std': 10.051806449890137, 'layers/layer_fc3/activation_std': 14.125566482543945, 'layers/layer_fc1.weight_mean': -4.3949068640358746e-05, 'layers/layer_fc1.bias_mean': 5.6177024816861376e-05, 'layers/layer_fc2.weight_mean': -3.007975647051353e-05, 'layers/layer_fc2.bias_mean': -6.0376849432941526e-05, 'layers/layer_fc3.weight_mean': -6.851441867183894e-06, 'layers/layer_fc3.bias_mean': -4.150162567384541e-05, 'layers/layer_fc4.weight_mean': -3.618385744630359e-05, 'layers/layer_fc4.bias_mean': -9.973671694751829e-05, 'layers/layer_fc5.weight_mean': 1.3166733259240004e-09, 'layers/layer_fc5.bias_mean': 1.3737008197622913e-09, 'layers/layer_fc1.weight_std': 0.0014514160575345159, 'layers/layer_fc1.bias_std': 0.001587417908012867, 'layers/layer_fc2.weight_std': 0.002324556466192007, 'layers/layer_fc2.bias_std': 0.000764612341299653, 'layers/layer_fc3.weight_std': 0.0007226017769426107, 'layers/layer_fc3.bias_std': 0.0007500615902245045, 'layers/layer_fc4.weight_std': 0.001422140165232122, 'layers/layer_fc4.bias_std': 0.000992378918454051, 'layers/layer_fc5.weight_std': 0.016280390322208405, 'layers/layer_fc5.bias_std': 0.013839689083397388, 'grad_norm/fc1.weight': 0.6505305767059326, 'grad_norm/fc1.bias': 0.025364963337779045, 'grad_norm/fc2.weight': 0.8416466116905212, 'grad_norm/fc2.bias': 0.017338205128908157, 'grad_norm/fc3.weight': 0.26162052154541016, 'grad_norm/fc3.bias': 0.01199591625481844, 'grad_norm/fc4.weight': 0.25751423835754395, 'grad_norm/fc4.bias': 0.011240324936807156, 'grad_norm/fc5.weight': 0.5822373628616333, 'grad_norm/fc5.bias': 0.04151906818151474}\n",
+      "Testing complete. Loss: 0.1949, Accuracy: 0.95%\n"
      ]
     }
    ],
@@ -450,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {

From 6f237612bf40e7b49e6ee1feca9974371fddb268 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 14:45:39 +0100
Subject: [PATCH 023/125] refactor: update packages for example

---
 .../pytorch_text_model_debugging.ipynb        | 305 ++++++++++--------
 1 file changed, 168 insertions(+), 137 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index adf4411..5c81aa8 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -2,17 +2,31 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install dependencies\n",
+    "! pip install -q -U neptune_scale torch datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
-    "import matplotlib.pyplot as plt\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
     "import numpy as np\n",
+    "from collections import Counter\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "from neptune_scale import Run\n"
+    "from neptune_scale import Run"
    ]
   },
   {
@@ -32,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,13 +64,14 @@
     "    \"input_size\": 28 * 28, # TODO - remove\n",
     "    \"vocab_size\": 300000,\n",
     "    \"embed_size\": 1000,\n",
-    "    \"hidden_size\": 256 # hidden size for the LSTM\n",
+    "    \"hidden_size\": 256, # hidden size for the LSTM\n",
+    "    \"dropout_prob\": 0.3\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +172,7 @@
     "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
     "        super(SimpleLLM, self).__init__()\n",
     "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, 512, batch_first=True)\n",
+    "        self.lstm = nn.LSTM(embed_size, 512, num_layers=1, dropout = params[\"dropout_prob\"], batch_first=True)\n",
     "        self.fc1 = nn.Linear(512, hidden_size)\n",
     "        self.fc2 = nn.Linear(hidden_size, vocab_size)\n",
     "\n",
@@ -178,7 +193,7 @@
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
     "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
-    "        out = self.fc1(lstm_out)\n",
+    "        out = self.fc1(lstm_out) # Use the last output from the LSTM\n",
     "        out = self.fc2(out)\n",
     "        return out\n",
     "    \n",
@@ -253,14 +268,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 144,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 38102,  83130, 136371, 174077, 163091],\n",
+      "        [115872, 149419,  90390, 125438, 109051],\n",
+      "        [ 21550, 231165,  31530,  59481, 190700],\n",
+      "        [264765,  13143, 148381, 191068,   1969],\n",
+      "        [263624, 146997, 290042, 192130,  34321],\n",
+      "        [ 34259, 255854, 192565, 292704, 182054],\n",
+      "        [259433, 242532,  90467,  38266,  10173],\n",
+      "        [ 81651, 133413, 117379,  57402, 234105]])\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "# Dummy input data (example with batch_size=3 and sequence_length=5)\n",
     "input_data = torch.randint(0, params[\"vocab_size\"], (params[\"batch_size\"] * 3, params[\"sequence_length\"]))  # Random word indices\n",
     "target_data = torch.randint(0, params[\"vocab_size\"], (params[\"batch_size\"] * 3, params[\"sequence_length\"]))  # Random target labels\n",
+    "\n",
+    "# Split the data into train and validation sets\n",
+    "train_input, val_input = train_test_split(input_data, test_size=0.1, random_state=42)\n",
+    "train_target, val_target = train_test_split(target_data, test_size=0.1, random_state=42)\n",
+    "\n",
+    "print(train_input)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
     "# Manually splitting the data into train, validation, and test sets\n",
     "train_size = int(0.6 * input_data.size(0))  # 60% for training\n",
@@ -280,136 +324,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/400, Loss: 12.6022\n",
-      "Epoch 2/400, Loss: 11.9868\n",
-      "Epoch 3/400, Loss: 11.3907\n",
-      "Epoch 4/400, Loss: 10.7466\n",
-      "Epoch 5/400, Loss: 10.0276\n",
-      "Epoch 6/400, Loss: 9.2259\n",
-      "Epoch 7/400, Loss: 8.3386\n",
-      "Epoch 8/400, Loss: 7.3620\n",
-      "Epoch 9/400, Loss: 6.2913\n",
-      "Epoch 10/400, Loss: 5.1245\n",
-      "Epoch 11/400, Loss: 3.8811\n",
-      "Epoch 12/400, Loss: 2.6654\n",
-      "Epoch 13/400, Loss: 1.6767\n",
-      "Epoch 14/400, Loss: 1.0357\n",
-      "Epoch 15/400, Loss: 0.6587\n",
-      "Epoch 16/400, Loss: 0.3819\n",
-      "Epoch 17/400, Loss: 0.1724\n",
-      "Epoch 18/400, Loss: 0.0595\n",
-      "Epoch 19/400, Loss: 0.0203\n",
-      "Epoch 20/400, Loss: 0.0097\n",
-      "Epoch 21/400, Loss: 0.0066\n",
-      "Epoch 22/400, Loss: 0.0053\n",
-      "Epoch 23/400, Loss: 0.0046\n",
-      "Epoch 24/400, Loss: 0.0041\n",
-      "Epoch 25/400, Loss: 0.0037\n",
-      "Epoch 26/400, Loss: 0.0033\n",
-      "Epoch 27/400, Loss: 0.0030\n",
-      "Epoch 28/400, Loss: 0.0027\n",
-      "Epoch 29/400, Loss: 0.0024\n",
-      "Epoch 30/400, Loss: 0.0022\n",
-      "Epoch 31/400, Loss: 0.0020\n",
-      "Epoch 32/400, Loss: 0.0018\n",
-      "Epoch 33/400, Loss: 0.0017\n",
-      "Epoch 34/400, Loss: 0.0015\n",
-      "Epoch 35/400, Loss: 0.0014\n",
-      "Epoch 36/400, Loss: 0.0013\n",
-      "Epoch 37/400, Loss: 0.0012\n",
-      "Epoch 38/400, Loss: 0.0012\n",
-      "Epoch 39/400, Loss: 0.0011\n",
-      "Epoch 40/400, Loss: 0.0010\n",
-      "Epoch 41/400, Loss: 0.0010\n",
-      "Epoch 42/400, Loss: 0.0009\n",
-      "Epoch 43/400, Loss: 0.0009\n",
-      "Epoch 44/400, Loss: 0.0008\n",
-      "Epoch 45/400, Loss: 0.0008\n",
-      "Epoch 46/400, Loss: 0.0008\n",
-      "Epoch 47/400, Loss: 0.0007\n",
-      "Epoch 48/400, Loss: 0.0007\n",
-      "Epoch 49/400, Loss: 0.0007\n",
-      "Epoch 50/400, Loss: 0.0007\n",
-      "Epoch 51/400, Loss: 0.0006\n",
-      "Epoch 52/400, Loss: 0.0006\n",
-      "Epoch 53/400, Loss: 0.0006\n",
-      "Epoch 54/400, Loss: 0.0006\n",
-      "Epoch 55/400, Loss: 0.0006\n",
-      "Epoch 56/400, Loss: 0.0005\n",
-      "Epoch 57/400, Loss: 0.0005\n",
-      "Epoch 58/400, Loss: 0.0005\n",
-      "Epoch 59/400, Loss: 0.0005\n",
-      "Epoch 60/400, Loss: 0.0005\n",
-      "Epoch 61/400, Loss: 0.0005\n",
-      "Epoch 62/400, Loss: 0.0005\n",
-      "Epoch 63/400, Loss: 0.0005\n",
-      "Epoch 64/400, Loss: 0.0005\n",
-      "Epoch 65/400, Loss: 0.0005\n",
-      "Epoch 66/400, Loss: 0.0004\n",
-      "Epoch 67/400, Loss: 0.0004\n",
-      "Epoch 68/400, Loss: 0.0004\n",
-      "Epoch 69/400, Loss: 0.0004\n",
-      "Epoch 70/400, Loss: 0.0004\n",
-      "Epoch 71/400, Loss: 0.0004\n",
-      "Epoch 72/400, Loss: 0.0004\n",
-      "Epoch 73/400, Loss: 0.0004\n",
-      "Epoch 74/400, Loss: 0.0004\n",
-      "Epoch 75/400, Loss: 0.0004\n",
-      "Epoch 76/400, Loss: 0.0004\n",
-      "Epoch 77/400, Loss: 0.0004\n",
-      "Epoch 78/400, Loss: 0.0004\n",
-      "Epoch 79/400, Loss: 0.0004\n",
-      "Epoch 80/400, Loss: 0.0004\n",
-      "Epoch 81/400, Loss: 0.0004\n",
-      "Epoch 82/400, Loss: 0.0004\n",
-      "Epoch 83/400, Loss: 0.0004\n",
-      "Epoch 84/400, Loss: 0.0004\n",
-      "Epoch 85/400, Loss: 0.0004\n",
-      "Epoch 86/400, Loss: 0.0003\n",
-      "Epoch 87/400, Loss: 0.0003\n",
-      "Epoch 88/400, Loss: 0.0003\n",
-      "Epoch 89/400, Loss: 0.0003\n",
-      "Epoch 90/400, Loss: 0.0003\n",
-      "Epoch 91/400, Loss: 0.0003\n",
-      "Epoch 92/400, Loss: 0.0003\n",
-      "Epoch 93/400, Loss: 0.0003\n",
-      "Epoch 94/400, Loss: 0.0003\n",
-      "Epoch 95/400, Loss: 0.0003\n",
-      "Epoch 96/400, Loss: 0.0003\n",
-      "Epoch 97/400, Loss: 0.0003\n",
-      "Epoch 98/400, Loss: 0.0003\n",
-      "Epoch 99/400, Loss: 0.0003\n",
-      "Epoch 100/400, Loss: 0.0003\n",
-      "Epoch 101/400, Loss: 0.0003\n",
-      "Epoch 102/400, Loss: 0.0003\n",
-      "Epoch 103/400, Loss: 0.0003\n",
-      "Epoch 104/400, Loss: 0.0003\n",
-      "Epoch 105/400, Loss: 0.0003\n",
-      "Epoch 106/400, Loss: 0.0003\n",
-      "Epoch 107/400, Loss: 0.0003\n",
-      "Epoch 108/400, Loss: 0.0003\n",
-      "Epoch 109/400, Loss: 0.0003\n",
-      "Epoch 110/400, Loss: 0.0003\n",
-      "Epoch 111/400, Loss: 0.0003\n",
-      "Epoch 112/400, Loss: 0.0003\n",
-      "Epoch 113/400, Loss: 0.0003\n",
-      "Epoch 114/400, Loss: 0.0003\n",
-      "Epoch 115/400, Loss: 0.0003\n",
-      "Epoch 116/400, Loss: 0.0003\n",
-      "Epoch 117/400, Loss: 0.0003\n",
-      "Epoch 118/400, Loss: 0.0003\n",
-      "Epoch 119/400, Loss: 0.0003\n",
-      "Epoch 120/400, Loss: 0.0003\n",
-      "Epoch 121/400, Loss: 0.0003\n",
-      "Epoch 122/400, Loss: 0.0003\n",
-      "Epoch 123/400, Loss: 0.0003\n"
+      "Epoch 1/400, Loss: 13.1628\n",
+      "Epoch 2/400, Loss: 12.5549\n",
+      "Epoch 3/400, Loss: 12.4428\n",
+      "Epoch 4/400, Loss: 12.2459\n",
+      "Epoch 5/400, Loss: 11.9436\n",
+      "Epoch 6/400, Loss: 11.6424\n",
+      "Epoch 7/400, Loss: 11.2635\n",
+      "Epoch 8/400, Loss: 10.8578\n",
+      "Epoch 9/400, Loss: 10.3417\n",
+      "Epoch 10/400, Loss: 9.7528\n",
+      "Epoch 11/400, Loss: 9.0635\n",
+      "Epoch 12/400, Loss: 8.2568\n",
+      "Epoch 13/400, Loss: 7.3002\n",
+      "Epoch 14/400, Loss: 6.2110\n",
+      "Epoch 15/400, Loss: 4.9925\n",
+      "Epoch 16/400, Loss: 3.7230\n",
+      "Epoch 17/400, Loss: 2.5503\n",
+      "Epoch 18/400, Loss: 1.5385\n",
+      "Epoch 19/400, Loss: 0.7866\n",
+      "Epoch 20/400, Loss: 0.4000\n",
+      "Epoch 21/400, Loss: 0.2285\n",
+      "Epoch 22/400, Loss: 0.1262\n",
+      "Epoch 23/400, Loss: 0.0624\n",
+      "Epoch 24/400, Loss: 0.0344\n",
+      "Epoch 25/400, Loss: 0.0240\n",
+      "Epoch 26/400, Loss: 0.0197\n",
+      "Epoch 27/400, Loss: 0.0177\n",
+      "Epoch 28/400, Loss: 0.0167\n",
+      "Epoch 29/400, Loss: 0.0155\n",
+      "Epoch 30/400, Loss: 0.0135\n",
+      "Epoch 31/400, Loss: 0.0110\n",
+      "Epoch 32/400, Loss: 0.0087\n",
+      "Epoch 33/400, Loss: 0.0071\n",
+      "Epoch 34/400, Loss: 0.0061\n",
+      "Epoch 35/400, Loss: 0.0055\n",
+      "Epoch 36/400, Loss: 0.0052\n",
+      "Epoch 37/400, Loss: 0.0049\n",
+      "Epoch 38/400, Loss: 0.0047\n",
+      "Epoch 39/400, Loss: 0.0044\n",
+      "Epoch 40/400, Loss: 0.0042\n",
+      "Epoch 41/400, Loss: 0.0040\n",
+      "Epoch 42/400, Loss: 0.0037\n",
+      "Epoch 43/400, Loss: 0.0035\n",
+      "Epoch 44/400, Loss: 0.0032\n",
+      "Epoch 45/400, Loss: 0.0030\n",
+      "Epoch 46/400, Loss: 0.0028\n",
+      "Epoch 47/400, Loss: 0.0026\n",
+      "Epoch 48/400, Loss: 0.0024\n",
+      "Epoch 49/400, Loss: 0.0022\n",
+      "Epoch 50/400, Loss: 0.0021\n",
+      "Epoch 51/400, Loss: 0.0019\n",
+      "Epoch 52/400, Loss: 0.0018\n",
+      "Epoch 53/400, Loss: 0.0017\n",
+      "Epoch 54/400, Loss: 0.0016\n",
+      "Epoch 55/400, Loss: 0.0016\n",
+      "Epoch 56/400, Loss: 0.0015\n",
+      "Epoch 57/400, Loss: 0.0014\n",
+      "Epoch 58/400, Loss: 0.0014\n",
+      "Epoch 59/400, Loss: 0.0013\n",
+      "Epoch 60/400, Loss: 0.0013\n",
+      "Epoch 61/400, Loss: 0.0012\n",
+      "Epoch 62/400, Loss: 0.0012\n",
+      "Epoch 63/400, Loss: 0.0011\n",
+      "Epoch 64/400, Loss: 0.0011\n",
+      "Epoch 65/400, Loss: 0.0011\n",
+      "Epoch 66/400, Loss: 0.0010\n",
+      "Epoch 67/400, Loss: 0.0010\n",
+      "Epoch 68/400, Loss: 0.0010\n",
+      "Epoch 69/400, Loss: 0.0010\n",
+      "Epoch 70/400, Loss: 0.0009\n",
+      "Epoch 71/400, Loss: 0.0009\n",
+      "Epoch 72/400, Loss: 0.0009\n",
+      "Epoch 73/400, Loss: 0.0009\n",
+      "Epoch 74/400, Loss: 0.0009\n",
+      "Epoch 75/400, Loss: 0.0008\n",
+      "Epoch 76/400, Loss: 0.0008\n",
+      "Epoch 77/400, Loss: 0.0008\n",
+      "Epoch 78/400, Loss: 0.0008\n",
+      "Epoch 79/400, Loss: 0.0008\n",
+      "Epoch 80/400, Loss: 0.0008\n",
+      "Epoch 81/400, Loss: 0.0008\n",
+      "Epoch 82/400, Loss: 0.0008\n",
+      "Epoch 83/400, Loss: 0.0007\n",
+      "Epoch 84/400, Loss: 0.0007\n",
+      "Epoch 85/400, Loss: 0.0007\n",
+      "Epoch 86/400, Loss: 0.0007\n",
+      "Epoch 87/400, Loss: 0.0007\n",
+      "Epoch 88/400, Loss: 0.0007\n",
+      "Epoch 89/400, Loss: 0.0007\n",
+      "Epoch 90/400, Loss: 0.0007\n",
+      "Epoch 91/400, Loss: 0.0007\n",
+      "Epoch 92/400, Loss: 0.0007\n",
+      "Epoch 93/400, Loss: 0.0007\n",
+      "Epoch 94/400, Loss: 0.0007\n",
+      "Epoch 95/400, Loss: 0.0007\n",
+      "Epoch 96/400, Loss: 0.0006\n",
+      "Epoch 97/400, Loss: 0.0006\n",
+      "Epoch 98/400, Loss: 0.0006\n",
+      "Epoch 99/400, Loss: 0.0006\n",
+      "Epoch 100/400, Loss: 0.0006\n",
+      "Epoch 101/400, Loss: 0.0006\n",
+      "Epoch 102/400, Loss: 0.0006\n",
+      "Epoch 103/400, Loss: 0.0006\n",
+      "Epoch 104/400, Loss: 0.0006\n"
      ]
     },
     {
@@ -419,7 +444,13 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[126], line 45\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, param \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mnamed_parameters():\n\u001b[0;32m     44\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m param \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 45\u001b[0m         gradients_std[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayers/layer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_std\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mparam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     46\u001b[0m         gradients_mean[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayers/layer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_mean\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m param\u001b[38;5;241m.\u001b[39mgrad\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     47\u001b[0m         gradient_norms[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayers/layer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_norm\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m param\u001b[38;5;241m.\u001b[39mgrad\u001b[38;5;241m.\u001b[39mnorm()\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;66;03m# L2 norm (Euclidean norm) of the gradients\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[142], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     28\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[1;32m---> 29\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     31\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m evaluate_model(model, val_input, val_target, params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvocab_size\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:478\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    476\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (exp_avg_sq\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[1;32m--> 478\u001b[0m     \u001b[43mparam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maddcdiv_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp_avg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdenom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mstep_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    480\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n\u001b[0;32m    481\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m amsgrad \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mis_complex(params[i]):\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -496,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {

From 2c82a0f8f0fe9d7795592b94d025c8397e3a7e5a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 14:48:32 +0100
Subject: [PATCH 024/125] refactor: update dataset to be used in example

---
 .../pytorch_text_model_debugging.ipynb        | 89 +++++++++++--------
 1 file changed, 50 insertions(+), 39 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 5c81aa8..2b51e73 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -266,37 +266,23 @@
     "model.fc.register_full_backward_hook(capture_gradient('fc'))\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download or use next token prediction dataset\n",
+    "The dataset used in this example is taken from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset). In this example, you can increase the size of the dataset to test the logging capabilities of Neptyune, but note that increasing the dataset size will increase the time taken for the full dataset to download."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[ 38102,  83130, 136371, 174077, 163091],\n",
-      "        [115872, 149419,  90390, 125438, 109051],\n",
-      "        [ 21550, 231165,  31530,  59481, 190700],\n",
-      "        [264765,  13143, 148381, 191068,   1969],\n",
-      "        [263624, 146997, 290042, 192130,  34321],\n",
-      "        [ 34259, 255854, 192565, 292704, 182054],\n",
-      "        [259433, 242532,  90467,  38266,  10173],\n",
-      "        [ 81651, 133413, 117379,  57402, 234105]])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "\n",
-    "# Dummy input data (example with batch_size=3 and sequence_length=5)\n",
-    "input_data = torch.randint(0, params[\"vocab_size\"], (params[\"batch_size\"] * 3, params[\"sequence_length\"]))  # Random word indices\n",
-    "target_data = torch.randint(0, params[\"vocab_size\"], (params[\"batch_size\"] * 3, params[\"sequence_length\"]))  # Random target labels\n",
-    "\n",
-    "# Split the data into train and validation sets\n",
-    "train_input, val_input = train_test_split(input_data, test_size=0.1, random_state=42)\n",
-    "train_target, val_target = train_test_split(target_data, test_size=0.1, random_state=42)\n",
-    "\n",
-    "print(train_input)\n"
+    "# For the example, download a random subset of 10% of the original dataset\n",
+    "train_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"train[:2%]\")\n",
+    "validation_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"validation[:2%]\")"
    ]
   },
   {
@@ -305,21 +291,46 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "class TokenizedDataset(Dataset):\n",
+    "    def __init__(self, tokenized_data):\n",
+    "        self.data = tokenized_data\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.data)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        item = self.data[idx]\n",
+    "        # Return input_ids, attention_mask, and labels as tensors\n",
+    "        return {\n",
+    "            'input_ids': torch.tensor(item['input_ids'], dtype=torch.long),\n",
+    "            'attention_mask': torch.tensor(item['attention_mask'], dtype=torch.long),\n",
+    "            'labels': torch.tensor(item['labels'], dtype=torch.long)\n",
+    "        }\n",
+    "    \n",
+    "# Assuming tokenized_data is a list of dictionaries, each containing 'input_ids', 'attention_mask', and 'labels'\n",
+    "train_dataset = TokenizedDataset(train_subset)  # tokenized_data is the list you want to use\n",
+    "train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
     "\n",
-    "# Manually splitting the data into train, validation, and test sets\n",
-    "train_size = int(0.6 * input_data.size(0))  # 60% for training\n",
-    "val_size = int(0.2 * input_data.size(0))    # 20% for validation\n",
-    "test_size = input_data.size(0) - train_size - val_size  # Remaining 20% for testing\n",
-    "\n",
-    "# Creating the splits manually\n",
-    "train_input = input_data[:train_size]\n",
-    "train_target = target_data[:train_size]\n",
+    "val_dataset = TokenizedDataset(validation_subset)  # tokenized_data is the list you want to use\n",
+    "val_dataloader = DataLoader(val_dataset, batch_size=8, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Determine the vocab size of the dataset\n",
+    "# Flatten the list of tokenized sentences into one long list of token IDs\n",
+    "all_tokens = [token for sentence in train_subset[\"input_ids\"] for token in sentence]\n",
     "\n",
-    "val_input = input_data[train_size:train_size + val_size]\n",
-    "val_target = target_data[train_size:train_size + val_size]\n",
+    "# Get unique token IDs\n",
+    "unique_tokens = set(all_tokens)\n",
     "\n",
-    "test_input = input_data[train_size + val_size:]\n",
-    "test_target = target_data[train_size + val_size:]\n"
+    "# Vocab size is the number of unique tokens\n",
+    "vocab_size = max(unique_tokens) + 1 # Add 1 since token ID's start from zero\n",
+    "print(f\"Vocabulary size: {vocab_size}\")"
    ]
   },
   {

From 48451d624a6453da303125dab570529d39afd194 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 14:51:03 +0100
Subject: [PATCH 025/125] refactor: update evalution function that calculates
 the validation losses using the data loader

---
 .../pytorch_text_model_debugging.ipynb        | 47 ++++++++++++-------
 1 file changed, 31 insertions(+), 16 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 2b51e73..da3905a 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,20 +144,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def evaluate_model(model, input, target, vocab_size):\n",
-    "    model.eval() # set model to evaluation mode\n",
-    "    with torch.no_grad(): # disable gradient calculation\n",
-    "        output = model(input)\n",
-    "        output = output.view(-1, vocab_size)\n",
-    "        data_target = target.view(-1)\n",
-    "        criterion = nn.CrossEntropyLoss()\n",
-    "        loss = criterion(output, data_target)\n",
-    "        \n",
-    "        return loss.item()\n",
+    "def evaluate(model, val_dataloader, criterion, device):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    total_loss = 0\n",
+    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
+    "        for batch in val_dataloader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "\n",
+    "            # Forward pass for validation\n",
+    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
+    "            \n",
+    "            # Calculate the loss\n",
+    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "    avg_val_loss = total_loss / len(val_dataloader)\n",
+    "    return avg_val_loss\n",
     "        "
    ]
   },
@@ -276,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,9 +324,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 150,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary size: 128257\n"
+     ]
+    }
+   ],
    "source": [
     "# Determine the vocab size of the dataset\n",
     "# Flatten the list of tokenized sentences into one long list of token IDs\n",

From 5b5fbdd9fd4f891a3802f825ea77da052c0bf76d Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 15:31:53 +0100
Subject: [PATCH 026/125] refactor: update training loop to work with new data

---
 .../pytorch_text_model_debugging.ipynb        | 284 +++++-------------
 1 file changed, 81 insertions(+), 203 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index da3905a..0e5648d 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -35,7 +35,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO - improve dataset to be more realistic\n",
     "# TODO - capture the folowings; attentions, learning rate (per layer if needed)\n",
     "    # complete - activations, grad norms, gradients - weights+biases, loss - agg\n",
     "# TODO - capture per layer losses\n",
@@ -46,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,15 +53,14 @@
     "\n",
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 3,\n",
-    "    \"sequence_length\": 5,\n",
+    "    \"batch_size\": 3, # TODO - remove\n",
+    "    \"sequence_length\": 5, # TODO - remove\n",
     "    \"learning_rate\": 0.001,\n",
-    "    \"epochs\": 400, \n",
+    "    \"epochs\": 5, \n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
     "    \"n_classes\": 10, # TODO - remove\n",
     "    \"input_size\": 28 * 28, # TODO - remove\n",
-    "    \"vocab_size\": 300000,\n",
     "    \"embed_size\": 1000,\n",
     "    \"hidden_size\": 256, # hidden size for the LSTM\n",
     "    \"dropout_prob\": 0.3\n",
@@ -71,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +90,7 @@
     "        \"config/sequece_length\": params[\"sequence_length\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
     "        \"config/input_size\": params[\"input_size\"],\n",
-    "        \"data/vocab_size\": params[\"vocab_size\"],\n",
+    "        # \"data/vocab_size\": params[\"vocab_size\"],\n",
     "        \"data/embed_size\": params[\"embed_size\"]\n",
     "    }\n",
     ")\n",
@@ -144,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,9 +191,9 @@
     "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
     "\n",
     "        ## Registering hooks to track gradients\n",
-    "        self.hooks.append(self.lstm.register_full_backward_hook(self.save_gradient(\"lstm\")))\n",
-    "        self.hooks.append(self.fc1.register_full_backward_hook(self.save_gradient(\"fc1\")))\n",
-    "        self.hooks.append(self.fc2.register_full_backward_hook(self.save_gradient(\"fc2\")))\n",
+    "        #self.hooks.append(self.lstm.register_full_backward_hook(self.save_gradient(\"lstm\")))\n",
+    "        #self.hooks.append(self.fc1.register_full_backward_hook(self.save_gradient(\"fc1\")))\n",
+    "        #self.hooks.append(self.fc2.register_full_backward_hook(self.save_gradient(\"fc2\")))\n",
     "        \n",
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
@@ -229,9 +227,9 @@
     "        self.gradients = {}\n",
     "\n",
     "\n",
-    "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"])\n",
+    "model = SimpleLLM(vocab_size, params[\"embed_size\"], params[\"hidden_size\"])\n",
     "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
-    "criterion = nn.CrossEntropyLoss()\n"
+    "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n"
    ]
   },
   {
@@ -350,137 +348,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/400, Loss: 13.1628\n",
-      "Epoch 2/400, Loss: 12.5549\n",
-      "Epoch 3/400, Loss: 12.4428\n",
-      "Epoch 4/400, Loss: 12.2459\n",
-      "Epoch 5/400, Loss: 11.9436\n",
-      "Epoch 6/400, Loss: 11.6424\n",
-      "Epoch 7/400, Loss: 11.2635\n",
-      "Epoch 8/400, Loss: 10.8578\n",
-      "Epoch 9/400, Loss: 10.3417\n",
-      "Epoch 10/400, Loss: 9.7528\n",
-      "Epoch 11/400, Loss: 9.0635\n",
-      "Epoch 12/400, Loss: 8.2568\n",
-      "Epoch 13/400, Loss: 7.3002\n",
-      "Epoch 14/400, Loss: 6.2110\n",
-      "Epoch 15/400, Loss: 4.9925\n",
-      "Epoch 16/400, Loss: 3.7230\n",
-      "Epoch 17/400, Loss: 2.5503\n",
-      "Epoch 18/400, Loss: 1.5385\n",
-      "Epoch 19/400, Loss: 0.7866\n",
-      "Epoch 20/400, Loss: 0.4000\n",
-      "Epoch 21/400, Loss: 0.2285\n",
-      "Epoch 22/400, Loss: 0.1262\n",
-      "Epoch 23/400, Loss: 0.0624\n",
-      "Epoch 24/400, Loss: 0.0344\n",
-      "Epoch 25/400, Loss: 0.0240\n",
-      "Epoch 26/400, Loss: 0.0197\n",
-      "Epoch 27/400, Loss: 0.0177\n",
-      "Epoch 28/400, Loss: 0.0167\n",
-      "Epoch 29/400, Loss: 0.0155\n",
-      "Epoch 30/400, Loss: 0.0135\n",
-      "Epoch 31/400, Loss: 0.0110\n",
-      "Epoch 32/400, Loss: 0.0087\n",
-      "Epoch 33/400, Loss: 0.0071\n",
-      "Epoch 34/400, Loss: 0.0061\n",
-      "Epoch 35/400, Loss: 0.0055\n",
-      "Epoch 36/400, Loss: 0.0052\n",
-      "Epoch 37/400, Loss: 0.0049\n",
-      "Epoch 38/400, Loss: 0.0047\n",
-      "Epoch 39/400, Loss: 0.0044\n",
-      "Epoch 40/400, Loss: 0.0042\n",
-      "Epoch 41/400, Loss: 0.0040\n",
-      "Epoch 42/400, Loss: 0.0037\n",
-      "Epoch 43/400, Loss: 0.0035\n",
-      "Epoch 44/400, Loss: 0.0032\n",
-      "Epoch 45/400, Loss: 0.0030\n",
-      "Epoch 46/400, Loss: 0.0028\n",
-      "Epoch 47/400, Loss: 0.0026\n",
-      "Epoch 48/400, Loss: 0.0024\n",
-      "Epoch 49/400, Loss: 0.0022\n",
-      "Epoch 50/400, Loss: 0.0021\n",
-      "Epoch 51/400, Loss: 0.0019\n",
-      "Epoch 52/400, Loss: 0.0018\n",
-      "Epoch 53/400, Loss: 0.0017\n",
-      "Epoch 54/400, Loss: 0.0016\n",
-      "Epoch 55/400, Loss: 0.0016\n",
-      "Epoch 56/400, Loss: 0.0015\n",
-      "Epoch 57/400, Loss: 0.0014\n",
-      "Epoch 58/400, Loss: 0.0014\n",
-      "Epoch 59/400, Loss: 0.0013\n",
-      "Epoch 60/400, Loss: 0.0013\n",
-      "Epoch 61/400, Loss: 0.0012\n",
-      "Epoch 62/400, Loss: 0.0012\n",
-      "Epoch 63/400, Loss: 0.0011\n",
-      "Epoch 64/400, Loss: 0.0011\n",
-      "Epoch 65/400, Loss: 0.0011\n",
-      "Epoch 66/400, Loss: 0.0010\n",
-      "Epoch 67/400, Loss: 0.0010\n",
-      "Epoch 68/400, Loss: 0.0010\n",
-      "Epoch 69/400, Loss: 0.0010\n",
-      "Epoch 70/400, Loss: 0.0009\n",
-      "Epoch 71/400, Loss: 0.0009\n",
-      "Epoch 72/400, Loss: 0.0009\n",
-      "Epoch 73/400, Loss: 0.0009\n",
-      "Epoch 74/400, Loss: 0.0009\n",
-      "Epoch 75/400, Loss: 0.0008\n",
-      "Epoch 76/400, Loss: 0.0008\n",
-      "Epoch 77/400, Loss: 0.0008\n",
-      "Epoch 78/400, Loss: 0.0008\n",
-      "Epoch 79/400, Loss: 0.0008\n",
-      "Epoch 80/400, Loss: 0.0008\n",
-      "Epoch 81/400, Loss: 0.0008\n",
-      "Epoch 82/400, Loss: 0.0008\n",
-      "Epoch 83/400, Loss: 0.0007\n",
-      "Epoch 84/400, Loss: 0.0007\n",
-      "Epoch 85/400, Loss: 0.0007\n",
-      "Epoch 86/400, Loss: 0.0007\n",
-      "Epoch 87/400, Loss: 0.0007\n",
-      "Epoch 88/400, Loss: 0.0007\n",
-      "Epoch 89/400, Loss: 0.0007\n",
-      "Epoch 90/400, Loss: 0.0007\n",
-      "Epoch 91/400, Loss: 0.0007\n",
-      "Epoch 92/400, Loss: 0.0007\n",
-      "Epoch 93/400, Loss: 0.0007\n",
-      "Epoch 94/400, Loss: 0.0007\n",
-      "Epoch 95/400, Loss: 0.0007\n",
-      "Epoch 96/400, Loss: 0.0006\n",
-      "Epoch 97/400, Loss: 0.0006\n",
-      "Epoch 98/400, Loss: 0.0006\n",
-      "Epoch 99/400, Loss: 0.0006\n",
-      "Epoch 100/400, Loss: 0.0006\n",
-      "Epoch 101/400, Loss: 0.0006\n",
-      "Epoch 102/400, Loss: 0.0006\n",
-      "Epoch 103/400, Loss: 0.0006\n",
-      "Epoch 104/400, Loss: 0.0006\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[142], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     28\u001b[0m \u001b[38;5;66;03m# Optimizer step\u001b[39;00m\n\u001b[1;32m---> 29\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     31\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m evaluate_model(model, val_input, val_target, params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvocab_size\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:478\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    476\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (exp_avg_sq\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[1;32m--> 478\u001b[0m     \u001b[43mparam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maddcdiv_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp_avg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdenom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mstep_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    480\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n\u001b[0;32m    481\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m amsgrad \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mis_complex(params[i]):\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "activation_dict_mean = {}\n",
     "activation_dict_std = {}\n",
@@ -489,71 +359,79 @@
     "gradients_std = {}\n",
     "gradient_norms = {}\n",
     "\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "model.to(device)\n",
+    "step_counter = 0\n",
+    "\n",
     "# Training loop\n",
     "for epoch in range(params[\"epochs\"]):\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
-    "    model.clear_activations()\n",
-    "    model.clear_gradients()\n",
-    "\n",
-    "    # Forward pass\n",
-    "    output = model(train_input)\n",
-    "    \n",
-    "    # Reshape output and target to match the shape of CrossEntropyLoss expectations\n",
-    "    output = output.view(-1, params[\"vocab_size\"])  # Flatten the output\n",
-    "    target_data = train_target.view(-1)    # Flatten the target data\n",
-    "    \n",
-    "    # Calculate loss\n",
-    "    loss = criterion(output, target_data)\n",
-    "    \n",
-    "    # Backward pass\n",
-    "    loss.backward()\n",
-    "\n",
-    "    # Optimizer step\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    val_loss = evaluate_model(model, val_input, val_target, params[\"vocab_size\"])\n",
-    "\n",
-    "    # Track activations\n",
-    "    for name, activation in model.get_activations().items():\n",
-    "        activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
-    "        activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
-    "\n",
-    "    # Track gradient norm\n",
-    "    for layer, gradient in model.get_gradients().items():\n",
-    "        grad_norms[f\"grad_norm/{name}\"] = gradient.norm().item()\n",
-    "\n",
-    "    # Track gradients per layer at each epoch\n",
-    "    for name, param in model.named_parameters():\n",
-    "        if param is not None:\n",
-    "            gradients_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
-    "            gradients_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "            gradient_norms[f\"layers/layer_{name}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
-    "\n",
-    "    # Output loss for this epoch\n",
-    "    run.log_metrics(\n",
-    "        data = {\n",
-    "            \"metrics/train/loss\": loss.item(),\n",
-    "            \"metrics/validation/loss\": val_loss,\n",
-    "            \"epoch/value\": epoch,\n",
-    "            **activation_dict_mean,\n",
-    "            **activation_dict_std,\n",
-    "            **grad_norms,\n",
-    "            **gradients_std,\n",
-    "            **gradients_mean,\n",
-    "            **gradient_norms\n",
-    "        },\n",
-    "        step = epoch\n",
-    "    )\n",
-    "    print(f'Epoch {epoch+1}/{params[\"epochs\"]}, Loss: {loss.item():.4f}')\n",
+    "    model.train()\n",
+    "    total_loss = 0\n",
+    "    for batch in train_dataloader:\n",
+    "        step_counter += 1\n",
     "\n",
-    "test_loss = evaluate_model(model, test_input, test_target, params[\"vocab_size\"])\n",
-    "print(f'Test Loss: {test_loss:.4f}')\n"
+    "        input_ids = batch['input_ids'].to(device)\n",
+    "        labels = batch['labels'].to(device)\n",
+    "        \n",
+    "        optimizer.zero_grad()\n",
+    "        \n",
+    "        # Forward pass\n",
+    "        logits = model(input_ids)\n",
+    "        \n",
+    "        # Compute the loss (ignore padding tokens by masking labels)\n",
+    "        loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "        \n",
+    "        # Backward pass and optimization\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        total_loss += loss.item()\n",
+    "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
+    "\n",
+    "        if step_counter % 5 == 0: # Do not need to log validation at every step, although we can\n",
+    "            val_loss = evaluate(model, val_dataloader, criterion, device)\n",
+    "            print(f\"Step {step_counter}, Val_loss: {val_loss}\")\n",
+    "\n",
+    "            # Track activations\n",
+    "            for name, activation in model.get_activations().items():\n",
+    "                activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
+    "                activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
+    "\n",
+    "            # Track gradient norm\n",
+    "            # for layer, gradient in model.get_gradients().items():\n",
+    "             #   grad_norms[f\"grad_norm/{name}\"] = gradient.norm().item()\n",
+    "\n",
+    "            # Track gradients per layer at each epoch\n",
+    "            for name, param in model.named_parameters():\n",
+    "                if param is not None:\n",
+    "                    gradients_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
+    "                    gradients_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
+    "                    gradient_norms[f\"layers/layer_{name}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
+    "\n",
+    "            # Output loss for this epoch\n",
+    "            run.log_metrics(\n",
+    "                data = {\n",
+    "                    \"metrics/train/loss\": loss.item(),\n",
+    "                    \"metrics/validation/loss\": val_loss,\n",
+    "                    \"epoch/value\": epoch,\n",
+    "                    **activation_dict_mean,\n",
+    "                    **activation_dict_std,\n",
+    "                    # **grad_norms,\n",
+    "                    **gradients_std,\n",
+    "                    **gradients_mean,\n",
+    "                    **gradient_norms\n",
+    "                },\n",
+    "                step = step_counter\n",
+    "            )\n",
+    "    print(f\"Epoch {epoch + 1}, Loss: {total_loss / len(train_dataloader)}\")\n",
+    "\n",
+    "# test_loss = evaluate_model(model, test_input, test_target, params[\"vocab_size\"])\n",
+    "# print(f'Test Loss: {test_loss:.4f}')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {

From 8ff6f303c5b5defc81f8a30e66f8e790df390d32 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 15:50:17 +0100
Subject: [PATCH 027/125] chore: remove unused sections

---
 .../pytorch_text_model_debugging.ipynb        | 126 +++++++++++-------
 1 file changed, 78 insertions(+), 48 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 0e5648d..2b56f9d 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,45 +232,6 @@
     "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Define a function to capture activations\n",
-    "def capture_activation(name):\n",
-    "    def hook(model, input, output):\n",
-    "        activations.append((name, output))  # Capture activation\n",
-    "    return hook\n",
-    "\n",
-    "# Define a function to capture gradients\n",
-    "def capture_gradient(name):\n",
-    "    def hook(module, grad_input, grad_output):\n",
-    "        gradients.append((name, grad_output))  # Capture gradients\n",
-    "    return hook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Container for capturing activations and gradients\n",
-    "activations = []\n",
-    "gradients = []\n",
-    "\n",
-    "# Register hooks for LSTM and final Linear layer\n",
-    "model.lstm.register_forward_hook(capture_activation('lstm'))\n",
-    "model.fc.register_forward_hook(capture_activation('fc'))\n",
-    "\n",
-    "model.lstm.register_full_backward_hook(capture_gradient('lstm'))\n",
-    "model.fc.register_full_backward_hook(capture_gradient('fc'))\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -281,18 +242,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
-    "train_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"train[:2%]\")\n",
-    "validation_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"validation[:2%]\")"
+    "train_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"train[:1%]\")\n",
+    "validation_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"validation[:1%]\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
@@ -348,9 +309,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 170,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 1 / 6862, Loss: 8.108406066894531\n",
+      "Step 2 / 6862, Loss: 8.41750431060791\n",
+      "Step 3 / 6862, Loss: 7.808852195739746\n",
+      "Step 4 / 6862, Loss: 7.6255950927734375\n",
+      "Step 5 / 6862, Loss: 7.981787204742432\n",
+      "Step 5, Val_loss: 8.222261428833008\n",
+      "Step 6 / 6862, Loss: 8.22750473022461\n",
+      "Step 7 / 6862, Loss: 8.084123611450195\n",
+      "Step 8 / 6862, Loss: 8.00427532196045\n",
+      "Step 9 / 6862, Loss: 8.61939811706543\n",
+      "Step 10 / 6862, Loss: 8.01023006439209\n",
+      "Step 10, Val_loss: 8.182218432426453\n",
+      "Step 11 / 6862, Loss: 8.989625930786133\n",
+      "Step 12 / 6862, Loss: 8.146134376525879\n",
+      "Step 13 / 6862, Loss: 8.266355514526367\n",
+      "Step 14 / 6862, Loss: 8.179316520690918\n",
+      "Step 15 / 6862, Loss: 8.195590019226074\n",
+      "Step 15, Val_loss: 8.146348039309183\n",
+      "Step 16 / 6862, Loss: 7.857874393463135\n",
+      "Step 17 / 6862, Loss: 8.29499626159668\n",
+      "Step 18 / 6862, Loss: 8.928645133972168\n",
+      "Step 19 / 6862, Loss: 8.25019645690918\n",
+      "Step 20 / 6862, Loss: 8.065107345581055\n",
+      "Step 20, Val_loss: 8.129634300867716\n",
+      "Step 21 / 6862, Loss: 7.987353801727295\n",
+      "Step 22 / 6862, Loss: 8.142375946044922\n",
+      "Step 23 / 6862, Loss: 8.109880447387695\n",
+      "Step 24 / 6862, Loss: 8.218332290649414\n",
+      "Step 25 / 6862, Loss: 8.190092086791992\n",
+      "Step 25, Val_loss: 8.09989575544993\n",
+      "Step 26 / 6862, Loss: 7.514364242553711\n",
+      "Step 27 / 6862, Loss: 7.963650226593018\n",
+      "Step 28 / 6862, Loss: 7.415898323059082\n",
+      "Step 29 / 6862, Loss: 8.256692886352539\n",
+      "Step 30 / 6862, Loss: 7.928523063659668\n",
+      "Step 30, Val_loss: 8.071863492329916\n",
+      "Step 31 / 6862, Loss: 8.20297622680664\n",
+      "Step 32 / 6862, Loss: 7.520443439483643\n",
+      "Step 33 / 6862, Loss: 8.82754898071289\n",
+      "Step 34 / 6862, Loss: 7.743387699127197\n",
+      "Step 35 / 6862, Loss: 8.292647361755371\n",
+      "Step 35, Val_loss: 8.03139074643453\n",
+      "Step 36 / 6862, Loss: 7.725708961486816\n",
+      "Step 37 / 6862, Loss: 8.10973834991455\n",
+      "Step 38 / 6862, Loss: 8.47884464263916\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[170], line 32\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[0;32m     31\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 32\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     34\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:425\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m    423\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mlerp_(grad, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m device_beta1)\n\u001b[1;32m--> 425\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maddcmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconj\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[0;32m    428\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "activation_dict_mean = {}\n",
     "activation_dict_std = {}\n",
@@ -431,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {

From b32b27bd934de77d31381f9aea42659540b83b54 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 15:54:10 +0100
Subject: [PATCH 028/125] chore: re-organize notebook layout

---
 .../pytorch_text_model_debugging.ipynb        | 181 +++++++++---------
 1 file changed, 91 insertions(+), 90 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 2b56f9d..787088a 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -43,62 +43,6 @@
     "# TODO - batch normalization"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize model, loss function, and optimizer\n",
-    "\n",
-    "params = {\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 3, # TODO - remove\n",
-    "    \"sequence_length\": 5, # TODO - remove\n",
-    "    \"learning_rate\": 0.001,\n",
-    "    \"epochs\": 5, \n",
-    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
-    "    \"input_features\": 256,\n",
-    "    \"n_classes\": 10, # TODO - remove\n",
-    "    \"input_size\": 28 * 28, # TODO - remove\n",
-    "    \"embed_size\": 1000,\n",
-    "    \"hidden_size\": 256, # hidden size for the LSTM\n",
-    "    \"dropout_prob\": 0.3\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define Neptune parameters\n",
-    "from neptune_scale import Run\n",
-    "from uuid import uuid4\n",
-    "\n",
-    "run = Run(\n",
-    "    project = \"leo/pytorch-tutorial\",\n",
-    "    run_id=f\"pytorch-text-{uuid4()}\"\n",
-    "    )\n",
-    "\n",
-    "run.log_configs(\n",
-    "    {\n",
-    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
-    "        \"config/optimizer\": params[\"optimizer\"],\n",
-    "        \"config/batch_size\": params[\"batch_size\"],\n",
-    "        \"config/sequece_length\": params[\"sequence_length\"],\n",
-    "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/input_size\": params[\"input_size\"],\n",
-    "        # \"data/vocab_size\": params[\"vocab_size\"],\n",
-    "        \"data/embed_size\": params[\"embed_size\"]\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
-    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -142,33 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate(model, val_dataloader, criterion, device):\n",
-    "    model.eval()  # Set the model to evaluation mode\n",
-    "    total_loss = 0\n",
-    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
-    "        for batch in val_dataloader:\n",
-    "            input_ids = batch['input_ids'].to(device)\n",
-    "            labels = batch['labels'].to(device)\n",
-    "\n",
-    "            # Forward pass for validation\n",
-    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
-    "            \n",
-    "            # Calculate the loss\n",
-    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "            total_loss += loss.item()\n",
-    "\n",
-    "    avg_val_loss = total_loss / len(val_dataloader)\n",
-    "    return avg_val_loss\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,12 +142,7 @@
     "        return self.gradients\n",
     "    \n",
     "    def clear_gradients(self):\n",
-    "        self.gradients = {}\n",
-    "\n",
-    "\n",
-    "model = SimpleLLM(vocab_size, params[\"embed_size\"], params[\"hidden_size\"])\n",
-    "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
-    "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n"
+    "        self.gradients = {}\n"
    ]
   },
   {
@@ -309,7 +222,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize model, loss function, and optimizer\n",
+    "\n",
+    "params = {\n",
+    "    \"optimizer\": \"Adam\",\n",
+    "    \"batch_size\": 3, # TODO - remove\n",
+    "    \"sequence_length\": 5, # TODO - remove\n",
+    "    \"learning_rate\": 0.001,\n",
+    "    \"epochs\": 5, \n",
+    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+    "    \"input_features\": 256,\n",
+    "    \"n_classes\": 10, # TODO - remove\n",
+    "    \"input_size\": 28 * 28, # TODO - remove\n",
+    "    \"embed_size\": 1000,\n",
+    "    \"hidden_size\": 256, # hidden size for the LSTM\n",
+    "    \"dropout_prob\": 0.3,\n",
+    "    \"vocab_size\": vocab_size\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Neptune parameters\n",
+    "from neptune_scale import Run\n",
+    "from uuid import uuid4\n",
+    "\n",
+    "run = Run(\n",
+    "    project = \"leo/pytorch-tutorial\",\n",
+    "    run_id=f\"pytorch-text-{uuid4()}\"\n",
+    "    )\n",
+    "\n",
+    "run.log_configs(\n",
+    "    {\n",
+    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
+    "        \"config/optimizer\": params[\"optimizer\"],\n",
+    "        \"config/batch_size\": params[\"batch_size\"],\n",
+    "        \"config/sequece_length\": params[\"sequence_length\"],\n",
+    "        \"config/epochs\": params[\"epochs\"],\n",
+    "        \"config/input_size\": params[\"input_size\"],\n",
+    "        \"data/vocab_size\": params[\"vocab_size\"],\n",
+    "        \"data/embed_size\": params[\"embed_size\"]\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
+    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate(model, val_dataloader, criterion, device):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    total_loss = 0\n",
+    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
+    "        for batch in val_dataloader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "\n",
+    "            # Forward pass for validation\n",
+    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
+    "            \n",
+    "            # Calculate the loss\n",
+    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "    avg_val_loss = total_loss / len(val_dataloader)\n",
+    "    return avg_val_loss\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -389,6 +385,11 @@
     "gradients_std = {}\n",
     "gradient_norms = {}\n",
     "\n",
+    "# Initialize model and optimizer\n",
+    "model = SimpleLLM(vocab_size, params[\"embed_size\"], params[\"hidden_size\"])\n",
+    "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
+    "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n",
+    "\n",
     "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
     "model.to(device)\n",
     "step_counter = 0\n",

From 4d659a201724feb2a3a53b2520386eae5f376585 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 15:59:02 +0100
Subject: [PATCH 029/125] chore: cleanup and add parameters in right place

---
 .../pytorch/pytorch_text_model_debugging.ipynb    | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 787088a..1f7cd03 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -86,16 +86,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Define the simple LLM model with LSTM\n",
     "class SimpleLLM(nn.Module):\n",
-    "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
+    "    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):\n",
     "        super(SimpleLLM, self).__init__()\n",
     "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, 512, num_layers=1, dropout = params[\"dropout_prob\"], batch_first=True)\n",
+    "        self.lstm = nn.LSTM(embed_size, 512, num_layers = num_layers, dropout = 0.3, batch_first=True)\n",
     "        self.fc1 = nn.Linear(512, hidden_size)\n",
     "        self.fc2 = nn.Linear(hidden_size, vocab_size)\n",
     "\n",
@@ -132,7 +132,7 @@
     "    def clear_activations(self):\n",
     "        self.activations = {}\n",
     "\n",
-    "    # Function to save gradients\n",
+    "    # Function to save gradients TODO: check where gradients should be calculated\n",
     "    def save_gradient(self, name):\n",
     "        def hook(module, grad_input, grad_output):\n",
     "            self.gradients[name] = grad_output[0]  # Save gradient output in the dictionary\n",
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,8 @@
     "    \"embed_size\": 1000,\n",
     "    \"hidden_size\": 256, # hidden size for the LSTM\n",
     "    \"dropout_prob\": 0.3,\n",
-    "    \"vocab_size\": vocab_size\n",
+    "    \"vocab_size\": vocab_size,\n",
+    "    \"num_lstm_layers\": 1\n",
     "}"
    ]
   },
@@ -386,7 +387,7 @@
     "gradient_norms = {}\n",
     "\n",
     "# Initialize model and optimizer\n",
-    "model = SimpleLLM(vocab_size, params[\"embed_size\"], params[\"hidden_size\"])\n",
+    "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"])\n",
     "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
     "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n",
     "\n",

From 5e7ec6e542d448e23d2ac87741c4f28823be1fcf Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 16:02:23 +0100
Subject: [PATCH 030/125] refactor: add all debugging metrics to the same
 dictionary variable

---
 .../pytorch_text_model_debugging.ipynb        | 99 ++-----------------
 1 file changed, 10 insertions(+), 89 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 1f7cd03..588aa80 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,83 +308,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 1 / 6862, Loss: 8.108406066894531\n",
-      "Step 2 / 6862, Loss: 8.41750431060791\n",
-      "Step 3 / 6862, Loss: 7.808852195739746\n",
-      "Step 4 / 6862, Loss: 7.6255950927734375\n",
-      "Step 5 / 6862, Loss: 7.981787204742432\n",
-      "Step 5, Val_loss: 8.222261428833008\n",
-      "Step 6 / 6862, Loss: 8.22750473022461\n",
-      "Step 7 / 6862, Loss: 8.084123611450195\n",
-      "Step 8 / 6862, Loss: 8.00427532196045\n",
-      "Step 9 / 6862, Loss: 8.61939811706543\n",
-      "Step 10 / 6862, Loss: 8.01023006439209\n",
-      "Step 10, Val_loss: 8.182218432426453\n",
-      "Step 11 / 6862, Loss: 8.989625930786133\n",
-      "Step 12 / 6862, Loss: 8.146134376525879\n",
-      "Step 13 / 6862, Loss: 8.266355514526367\n",
-      "Step 14 / 6862, Loss: 8.179316520690918\n",
-      "Step 15 / 6862, Loss: 8.195590019226074\n",
-      "Step 15, Val_loss: 8.146348039309183\n",
-      "Step 16 / 6862, Loss: 7.857874393463135\n",
-      "Step 17 / 6862, Loss: 8.29499626159668\n",
-      "Step 18 / 6862, Loss: 8.928645133972168\n",
-      "Step 19 / 6862, Loss: 8.25019645690918\n",
-      "Step 20 / 6862, Loss: 8.065107345581055\n",
-      "Step 20, Val_loss: 8.129634300867716\n",
-      "Step 21 / 6862, Loss: 7.987353801727295\n",
-      "Step 22 / 6862, Loss: 8.142375946044922\n",
-      "Step 23 / 6862, Loss: 8.109880447387695\n",
-      "Step 24 / 6862, Loss: 8.218332290649414\n",
-      "Step 25 / 6862, Loss: 8.190092086791992\n",
-      "Step 25, Val_loss: 8.09989575544993\n",
-      "Step 26 / 6862, Loss: 7.514364242553711\n",
-      "Step 27 / 6862, Loss: 7.963650226593018\n",
-      "Step 28 / 6862, Loss: 7.415898323059082\n",
-      "Step 29 / 6862, Loss: 8.256692886352539\n",
-      "Step 30 / 6862, Loss: 7.928523063659668\n",
-      "Step 30, Val_loss: 8.071863492329916\n",
-      "Step 31 / 6862, Loss: 8.20297622680664\n",
-      "Step 32 / 6862, Loss: 7.520443439483643\n",
-      "Step 33 / 6862, Loss: 8.82754898071289\n",
-      "Step 34 / 6862, Loss: 7.743387699127197\n",
-      "Step 35 / 6862, Loss: 8.292647361755371\n",
-      "Step 35, Val_loss: 8.03139074643453\n",
-      "Step 36 / 6862, Loss: 7.725708961486816\n",
-      "Step 37 / 6862, Loss: 8.10973834991455\n",
-      "Step 38 / 6862, Loss: 8.47884464263916\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[170], line 32\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[0;32m     31\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 32\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     34\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:425\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m    423\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mlerp_(grad, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m device_beta1)\n\u001b[1;32m--> 425\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maddcmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconj\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[0;32m    428\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "activation_dict_mean = {}\n",
-    "activation_dict_std = {}\n",
-    "grad_norms = {}\n",
-    "gradients_mean = {}\n",
-    "gradients_std = {}\n",
-    "gradient_norms = {}\n",
+    "debug_metrics = {}\n",
     "\n",
     "# Initialize model and optimizer\n",
     "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"])\n",
@@ -426,8 +352,8 @@
     "\n",
     "            # Track activations\n",
     "            for name, activation in model.get_activations().items():\n",
-    "                activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
-    "                activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
+    "                debug_metrics[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
+    "                debug_metrics[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
     "\n",
     "            # Track gradient norm\n",
     "            # for layer, gradient in model.get_gradients().items():\n",
@@ -436,9 +362,9 @@
     "            # Track gradients per layer at each epoch\n",
     "            for name, param in model.named_parameters():\n",
     "                if param is not None:\n",
-    "                    gradients_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
-    "                    gradients_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "                    gradient_norms[f\"layers/layer_{name}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
+    "                    debug_metrics[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
+    "                    debug_metrics[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
+    "                    debug_metrics[f\"layers/layer_{name}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
     "\n",
     "            # Output loss for this epoch\n",
     "            run.log_metrics(\n",
@@ -446,12 +372,7 @@
     "                    \"metrics/train/loss\": loss.item(),\n",
     "                    \"metrics/validation/loss\": val_loss,\n",
     "                    \"epoch/value\": epoch,\n",
-    "                    **activation_dict_mean,\n",
-    "                    **activation_dict_std,\n",
-    "                    # **grad_norms,\n",
-    "                    **gradients_std,\n",
-    "                    **gradients_mean,\n",
-    "                    **gradient_norms\n",
+    "                    **debug_metrics\n",
     "                },\n",
     "                step = step_counter\n",
     "            )\n",
@@ -463,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {

From d27c2476a313bec6a97a9ea3d8b776ac5dec3ec7 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 5 Mar 2025 17:21:55 +0100
Subject: [PATCH 031/125] chore: change LSTM layers to see response in logging

---
 .../pytorch_text_model_debugging.ipynb        | 74 +++++++++++++++++--
 1 file changed, 67 insertions(+), 7 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 588aa80..a455f6d 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -220,9 +220,16 @@
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Everytime a new LSTM layer is added when updating the `num_lstm_layers` parameter is changed, 12 new metrics will be logged which correspond to mean, std and norm that are calculated. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 183,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,13 +249,13 @@
     "    \"hidden_size\": 256, # hidden size for the LSTM\n",
     "    \"dropout_prob\": 0.3,\n",
     "    \"vocab_size\": vocab_size,\n",
-    "    \"num_lstm_layers\": 1\n",
+    "    \"num_lstm_layers\": 3\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,6 +276,7 @@
     "        \"config/sequece_length\": params[\"sequence_length\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
     "        \"config/input_size\": params[\"input_size\"],\n",
+    "        \"config/num_lstm_layers\" : params[\"num_lstm_layers\"],\n",
     "        \"data/vocab_size\": params[\"vocab_size\"],\n",
     "        \"data/embed_size\": params[\"embed_size\"]\n",
     "    }\n",
@@ -280,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 179,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,9 +314,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 188,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 1 / 6862, Loss: 11.761494636535645\n",
+      "Step 2 / 6862, Loss: 11.750707626342773\n",
+      "Step 3 / 6862, Loss: 11.729766845703125\n",
+      "Step 4 / 6862, Loss: 11.664655685424805\n",
+      "Step 5 / 6862, Loss: 11.283023834228516\n",
+      "Step 5, Val_loss: 10.823039770126343\n",
+      "Step 6 / 6862, Loss: 10.765737533569336\n",
+      "Step 7 / 6862, Loss: 10.121588706970215\n",
+      "Step 8 / 6862, Loss: 9.69588565826416\n",
+      "Step 9 / 6862, Loss: 9.501729965209961\n",
+      "Step 10 / 6862, Loss: 10.076082229614258\n",
+      "Step 10, Val_loss: 9.637478192647299\n",
+      "Step 11 / 6862, Loss: 10.335653305053711\n",
+      "Step 12 / 6862, Loss: 9.704903602600098\n",
+      "Step 13 / 6862, Loss: 9.107070922851562\n",
+      "Step 14 / 6862, Loss: 8.985576629638672\n",
+      "Step 15 / 6862, Loss: 8.823647499084473\n",
+      "Step 15, Val_loss: 9.185553391774496\n",
+      "Step 16 / 6862, Loss: 9.310721397399902\n",
+      "Step 17 / 6862, Loss: 9.178205490112305\n",
+      "Step 18 / 6862, Loss: 9.248604774475098\n",
+      "Step 19 / 6862, Loss: 9.397645950317383\n",
+      "Step 20 / 6862, Loss: 9.491145133972168\n",
+      "Step 20, Val_loss: 9.07630173365275\n",
+      "Step 21 / 6862, Loss: 8.726667404174805\n",
+      "Step 22 / 6862, Loss: 8.85219955444336\n",
+      "Step 23 / 6862, Loss: 9.02513313293457\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[188], line 25\u001b[0m\n\u001b[0;32m     22\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Forward pass\u001b[39;00m\n\u001b[1;32m---> 25\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Compute the loss (ignore padding tokens by masking labels)\u001b[39;00m\n\u001b[0;32m     28\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "Cell \u001b[1;32mIn[172], line 28\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     26\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm(x)  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[0;32m     27\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc1(lstm_out) \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[1;32m---> 28\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfc2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "debug_metrics = {}\n",
     "\n",
@@ -384,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [
     {

From dc4b63d6724c81e2200fc53f27e20f3d4a9277a6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 6 Mar 2025 13:53:34 +0100
Subject: [PATCH 032/125] refactor: update location where model.train() is
 called in training loop

---
 .../pytorch/pytorch_text_model_debugging.ipynb                | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index a455f6d..aa1047e 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -383,9 +383,9 @@
     "\n",
     "# Training loop\n",
     "for epoch in range(params[\"epochs\"]):\n",
-    "    model.train()\n",
     "    total_loss = 0\n",
     "    for batch in train_dataloader:\n",
+    "        model.train()\n",
     "        step_counter += 1\n",
     "\n",
     "        input_ids = batch['input_ids'].to(device)\n",

From 1a8d02155083a14d8d6050d4b9a99c9947c4435a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 6 Mar 2025 16:12:16 +0100
Subject: [PATCH 033/125] refactor: update how HF dataset is downloaded to only
 download a subset

---
 .../pytorch_text_model_debugging.ipynb        | 70 ++++++++-----------
 1 file changed, 30 insertions(+), 40 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index aa1047e..c1ac4c7 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -155,18 +155,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
-    "train_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"train[:1%]\")\n",
-    "validation_subset = load_dataset(\"Na0s/Next_Token_Prediction_dataset\", split=\"validation[:1%]\")"
+    "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
+    "data_files = {\"train\": base_url + \"train-0000*-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
+    "              \"validation\": base_url + \"validation-00000-of-00001.parquet\"} #doanload the complete validation dataset\n",
+    "\n",
+    "data_subset = load_dataset(\"parquet\", data_files = data_files)\n",
+    "train_subset = data_subset[\"train\"]\n",
+    "validation_subset = data_subset[\"validation\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 208,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,40 +319,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 1 / 6862, Loss: 11.761494636535645\n",
-      "Step 2 / 6862, Loss: 11.750707626342773\n",
-      "Step 3 / 6862, Loss: 11.729766845703125\n",
-      "Step 4 / 6862, Loss: 11.664655685424805\n",
-      "Step 5 / 6862, Loss: 11.283023834228516\n",
-      "Step 5, Val_loss: 10.823039770126343\n",
-      "Step 6 / 6862, Loss: 10.765737533569336\n",
-      "Step 7 / 6862, Loss: 10.121588706970215\n",
-      "Step 8 / 6862, Loss: 9.69588565826416\n",
-      "Step 9 / 6862, Loss: 9.501729965209961\n",
-      "Step 10 / 6862, Loss: 10.076082229614258\n",
-      "Step 10, Val_loss: 9.637478192647299\n",
-      "Step 11 / 6862, Loss: 10.335653305053711\n",
-      "Step 12 / 6862, Loss: 9.704903602600098\n",
-      "Step 13 / 6862, Loss: 9.107070922851562\n",
-      "Step 14 / 6862, Loss: 8.985576629638672\n",
-      "Step 15 / 6862, Loss: 8.823647499084473\n",
-      "Step 15, Val_loss: 9.185553391774496\n",
-      "Step 16 / 6862, Loss: 9.310721397399902\n",
-      "Step 17 / 6862, Loss: 9.178205490112305\n",
-      "Step 18 / 6862, Loss: 9.248604774475098\n",
-      "Step 19 / 6862, Loss: 9.397645950317383\n",
-      "Step 20 / 6862, Loss: 9.491145133972168\n",
-      "Step 20, Val_loss: 9.07630173365275\n",
-      "Step 21 / 6862, Loss: 8.726667404174805\n",
-      "Step 22 / 6862, Loss: 8.85219955444336\n",
-      "Step 23 / 6862, Loss: 9.02513313293457\n"
+      "Step 1 / 102408, Loss: 11.768007278442383\n",
+      "Step 2 / 102408, Loss: 11.757434844970703\n",
+      "Step 3 / 102408, Loss: 11.730992317199707\n",
+      "Step 4 / 102408, Loss: 11.66014575958252\n",
+      "Step 5 / 102408, Loss: 11.353281021118164\n",
+      "Step 5, Val_loss: 10.716990483939087\n",
+      "Step 6 / 102408, Loss: 10.836886405944824\n",
+      "Step 7 / 102408, Loss: 10.088896751403809\n",
+      "Step 8 / 102408, Loss: 10.012699127197266\n",
+      "Step 9 / 102408, Loss: 10.076889991760254\n",
+      "Step 10 / 102408, Loss: 9.623441696166992\n"
      ]
     },
     {
@@ -357,14 +346,15 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[188], line 25\u001b[0m\n\u001b[0;32m     22\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Forward pass\u001b[39;00m\n\u001b[1;32m---> 25\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;66;03m# Compute the loss (ignore padding tokens by masking labels)\u001b[39;00m\n\u001b[0;32m     28\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
+      "Cell \u001b[1;32mIn[209], line 38\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_counter \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Do not need to log validation at every step, although we can\u001b[39;00m\n\u001b[1;32m---> 38\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     39\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Val_loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     41\u001b[0m     \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[208], line 10\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, val_dataloader, criterion, device)\u001b[0m\n\u001b[0;32m      7\u001b[0m labels \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Forward pass for validation\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Shape: (batch_size, seq_len, vocab_size)\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Calculate the loss\u001b[39;00m\n\u001b[0;32m     13\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "Cell \u001b[1;32mIn[172], line 28\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     26\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm(x)  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[0;32m     27\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc1(lstm_out) \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[1;32m---> 28\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfc2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "Cell \u001b[1;32mIn[172], line 26\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m     25\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[1;32m---> 26\u001b[0m     lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[0;32m     27\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc1(lstm_out) \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[0;32m     28\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc2(out)\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
       "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1124\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[1;34m(self, input, hx)\u001b[0m\n\u001b[0;32m   1121\u001b[0m         hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[0;32m   1123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1124\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1125\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1129\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1130\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1131\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1132\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1133\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1134\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1136\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\n\u001b[0;32m   1137\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m   1138\u001b[0m         batch_sizes,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1145\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional,\n\u001b[0;32m   1146\u001b[0m     )\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -444,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [
     {

From fd7cbc5ec2f1ccead949b22d8900598227355587 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 6 Mar 2025 16:23:24 +0100
Subject: [PATCH 034/125] refactor: update loading and processing of HF dataset
 to make code faster and more readable

---
 .../pytorch_text_model_debugging.ipynb        | 74 +++++--------------
 1 file changed, 18 insertions(+), 56 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index c1ac4c7..6cde57b 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,39 +164,12 @@
     "data_files = {\"train\": base_url + \"train-0000*-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
     "              \"validation\": base_url + \"validation-00000-of-00001.parquet\"} #doanload the complete validation dataset\n",
     "\n",
-    "data_subset = load_dataset(\"parquet\", data_files = data_files)\n",
-    "train_subset = data_subset[\"train\"]\n",
-    "validation_subset = data_subset[\"validation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class TokenizedDataset(Dataset):\n",
-    "    def __init__(self, tokenized_data):\n",
-    "        self.data = tokenized_data\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        item = self.data[idx]\n",
-    "        # Return input_ids, attention_mask, and labels as tensors\n",
-    "        return {\n",
-    "            'input_ids': torch.tensor(item['input_ids'], dtype=torch.long),\n",
-    "            'attention_mask': torch.tensor(item['attention_mask'], dtype=torch.long),\n",
-    "            'labels': torch.tensor(item['labels'], dtype=torch.long)\n",
-    "        }\n",
-    "    \n",
-    "# Assuming tokenized_data is a list of dictionaries, each containing 'input_ids', 'attention_mask', and 'labels'\n",
-    "train_dataset = TokenizedDataset(train_subset)  # tokenized_data is the list you want to use\n",
-    "train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
+    "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
+    "train_subset = data_subset[\"train\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
+    "validation_subset = data_subset[\"validation\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
     "\n",
-    "val_dataset = TokenizedDataset(validation_subset)  # tokenized_data is the list you want to use\n",
-    "val_dataloader = DataLoader(val_dataset, batch_size=8, shuffle=True)"
+    "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
+    "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)"
    ]
   },
   {
@@ -260,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 218,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,24 +292,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 219,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 1 / 102408, Loss: 11.768007278442383\n",
-      "Step 2 / 102408, Loss: 11.757434844970703\n",
-      "Step 3 / 102408, Loss: 11.730992317199707\n",
-      "Step 4 / 102408, Loss: 11.66014575958252\n",
-      "Step 5 / 102408, Loss: 11.353281021118164\n",
-      "Step 5, Val_loss: 10.716990483939087\n",
-      "Step 6 / 102408, Loss: 10.836886405944824\n",
-      "Step 7 / 102408, Loss: 10.088896751403809\n",
-      "Step 8 / 102408, Loss: 10.012699127197266\n",
-      "Step 9 / 102408, Loss: 10.076889991760254\n",
-      "Step 10 / 102408, Loss: 9.623441696166992\n"
+      "Step 1 / 102408, Loss: 11.76780891418457\n",
+      "Step 2 / 102408, Loss: 11.754159927368164\n"
      ]
     },
     {
@@ -346,15 +310,13 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[209], line 38\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_counter \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Do not need to log validation at every step, although we can\u001b[39;00m\n\u001b[1;32m---> 38\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     39\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Val_loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     41\u001b[0m     \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[208], line 10\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, val_dataloader, criterion, device)\u001b[0m\n\u001b[0;32m      7\u001b[0m labels \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Forward pass for validation\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Shape: (batch_size, seq_len, vocab_size)\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Calculate the loss\u001b[39;00m\n\u001b[0;32m     13\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "Cell \u001b[1;32mIn[172], line 26\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m     25\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[1;32m---> 26\u001b[0m     lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[0;32m     27\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc1(lstm_out) \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[0;32m     28\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc2(out)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1124\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[1;34m(self, input, hx)\u001b[0m\n\u001b[0;32m   1121\u001b[0m         hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[0;32m   1123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1124\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1125\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1129\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1130\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1131\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1132\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1133\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1134\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1136\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\n\u001b[0;32m   1137\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m   1138\u001b[0m         batch_sizes,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1145\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional,\n\u001b[0;32m   1146\u001b[0m     )\n",
+      "Cell \u001b[1;32mIn[219], line 32\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[0;32m     31\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 32\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     34\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:476\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    474\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (max_exp_avg_sqs[i]\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 476\u001b[0m         denom \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbias_correction2_sqrt\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_\u001b[49m\u001b[43m(\u001b[49m\u001b[43meps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    478\u001b[0m     param\u001b[38;5;241m.\u001b[39maddcdiv_(exp_avg, denom, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39mstep_size)\n\u001b[0;32m    480\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -434,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 220,
    "metadata": {},
    "outputs": [
     {

From 3be6c3e0dbaec91e3d81226b74451f6098337cbc Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 6 Mar 2025 18:18:26 +0100
Subject: [PATCH 035/125] chore: add TODO's to address

---
 .../pytorch/pytorch_text_model_debugging.ipynb              | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 6cde57b..f757ee6 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -40,7 +40,11 @@
     "# TODO - capture per layer losses\n",
     "# TODO - increase number of layers\n",
     "# TODO - Adding dropout to demonstrate the effects of regularization\n",
-    "# TODO - batch normalization"
+    "# TODO - batch normalization\n",
+    "# TODO - add class for tracking activations, gradients, etc. automatically\n",
+    "# TODO - investigate packages that help with tracking layer-wise information\n",
+    "# TODO - visualize layer-wise information, activations, gradients etc. (use as example to share with product)\n",
+    "# TODO - update hooks for model, ensure tracking correct information"
    ]
   },
   {

From 6e1df271d23e8c064917db4b20e7dd55b7205e59 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 11:12:41 +0100
Subject: [PATCH 036/125] chore: update sections

---
 .../pytorch_text_model_debugging.ipynb        | 142 +++++++++++-------
 1 file changed, 88 insertions(+), 54 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index f757ee6..9de1dc2 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -1,5 +1,32 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neptune + PyTorch\n",
+    "\n",
+    "## Logging and Visualizing debugging metrics with Neptune\n",
+    "\n",
+    "### Introduction\n",
+    "\n",
+    "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. \n",
+    "\n",
+    "This guide will show you how to:\n",
+    "- Initialize the Neptune Run object and log configuration parameters\n",
+    "- Log standard loss and accuracy metrics to Neptune\n",
+    "- Log debugging metrics during model training such as;\n",
+    "    * Activations per layer\n",
+    "    * Gradients (mean and std) per layer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install Dependencies and Import Libraries"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -12,10 +39,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Import libraries\n",
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
@@ -29,6 +57,35 @@
     "from neptune_scale import Run"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize model, loss function, and optimizer\n",
+    "\n",
+    "params = {\n",
+    "    \"optimizer\": \"Adam\",\n",
+    "    \"batch_size\": 3,\n",
+    "    \"learning_rate\": 0.001,\n",
+    "    \"epochs\": 5, \n",
+    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+    "    \"input_features\": 256,\n",
+    "    \"embed_size\": 1000,\n",
+    "    \"hidden_size\": 256, # hidden size for the LSTM\n",
+    "    \"dropout_prob\": 0.3,\n",
+    "    \"num_lstm_layers\": 3\n",
+    "}"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -51,41 +108,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "During training, capturing valuable information from each layer of a model can provide insights into how the model is learning, what features are being extracted, and how the model's performance can be improved. Researchers often look for certain metrics and activations from each layer, which can help in diagnosing issues such as vanishing gradients, overfitting, or underfitting.\n",
+    "## Importance of Logging Debugging Metrics\n",
     "\n",
-    "Key Information to Capture from Each Layer:\n",
-    "Activations:\n",
+    "During model training, capturing valuable information from each layer can offer critical insights into the model's learning process, identify potential issues, and guide improvements. Monitoring certain metrics and activations helps diagnose common problems such as vanishing gradients, overfitting, or underfitting. Below are key metrics to track from each layer:\n",
     "\n",
-    "What it shows: The activations of each layer during forward propagation can give insights into how the model is processing input data at different levels. By examining activations, you can see if certain layers are \"dead\" (i.e., not learning anything) or if activations are exploding (leading to unstable training).\n",
-    "How to capture: You can register hooks to capture activations after each layer.\n",
-    "Gradients:\n",
+    "### Key Metrics to Capture from Each Layer:\n",
     "\n",
-    "What it shows: The gradients of each layer during backpropagation are important for diagnosing issues like vanishing or exploding gradients. If gradients are too small, it can indicate the model is struggling to learn effectively (vanishing gradients). If they're too large, it can indicate instability (exploding gradients).\n",
-    "How to capture: Similar to activations, you can use hooks to capture gradients during backpropagation.\n",
-    "Weights and Biases:\n",
+    "- **Activations**\n",
+    "  - **What it shows**: Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability.\n",
+    "  - **How to capture**: Use hooks to capture activations after each layer.\n",
     "\n",
-    "What it shows: Tracking the weights and biases of each layer helps researchers understand how the model's parameters are evolving during training. For example, if weights are growing too large or becoming too small, it might suggest the need for better regularization or learning rate adjustments.\n",
-    "How to capture: You can extract weights and biases directly from the model’s parameters.\n",
-    "Layer-wise Loss:\n",
+    "- **Gradients**\n",
+    "  - **What it shows**: Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability.\n",
+    "  - **How to capture**: Use hooks to capture gradients during backpropagation.\n",
     "\n",
-    "What it shows: Tracking loss at different stages (layers) can help understand which parts of the network are contributing more to the overall loss. This can be valuable for debugging or optimizing the model.\n",
-    "How to capture: Loss can be tracked by monitoring the output from each layer and comparing it against the target.\n",
-    "Learning Rate per Layer:\n",
+    "- **Weights and Biases**\n",
+    "  - **What it shows**: Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate.\n",
+    "  - **How to capture**: Extract directly from the model’s parameters.\n",
     "\n",
-    "What it shows: If you're using techniques like Layer-wise Learning Rate Decay (L2LRD) or applying different learning rates to different layers, capturing the learning rate used by each layer during training can be informative.\n",
-    "How to capture: This information would depend on how the learning rate is defined in your optimizer, but it can be tracked manually.\n",
-    "Layer Output Norms:\n",
+    "- **Layer-wise Loss**\n",
+    "  - **What it shows**: Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization.\n",
+    "  - **How to capture**: Monitor outputs from each layer and compare with the target.\n",
     "\n",
-    "What it shows: Tracking the L2-norm (magnitude) of the output for each layer can help in detecting issues like gradient explosion or squashing effects. A norm that’s either too high or too low could suggest the model isn't behaving as expected.\n",
-    "How to capture: You can compute the L2-norm for the output of each layer.\n",
-    "Activation Distributions:\n",
+    "- **Learning Rate per Layer**\n",
+    "  - **What it shows**: Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate.\n",
+    "  - **How to capture**: Manually track based on optimizer settings.\n",
     "\n",
-    "What it shows: Visualizing or capturing the distribution of activations (e.g., mean, variance, or histograms) can reveal issues like the saturation of neurons in activation functions, especially in cases with ReLU (leading to dead neurons).\n",
-    "How to capture: You can visualize or compute statistical summaries of activations using Python libraries like matplotlib or seaborn.\n",
-    "Feature Maps (for Convolutional Layers):\n",
+    "- **Layer Output Norms**\n",
+    "  - **What it shows**: The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients.\n",
+    "  - **How to capture**: Compute the L2-norm for each layer’s output.\n",
     "\n",
-    "What it shows: If you have convolutional layers, tracking the feature maps can provide insight into how each filter is detecting specific patterns or features from the input data.\n",
-    "How to capture: You can visualize the feature maps after convolutional layers using libraries like matplotlib."
+    "- **Activation Distributions**\n",
+    "  - **What it shows**: Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons.\n",
+    "  - **How to capture**: Visualize or compute statistical summaries using tools like matplotlib or seaborn.\n",
+    "\n",
+    "- **Feature Maps (for Convolutional Layers)**\n",
+    "  - **What it shows**: Offers insights into how convolutional layers detect specific patterns in the data.\n",
+    "  - **How to capture**: Visualize feature maps after convolutional layers using libraries like matplotlib.\n"
    ]
   },
   {
@@ -178,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -199,6 +258,7 @@
     "\n",
     "# Vocab size is the number of unique tokens\n",
     "vocab_size = max(unique_tokens) + 1 # Add 1 since token ID's start from zero\n",
+    "params[\"vocab_size\"] = vocab_size\n",
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
   },
@@ -209,32 +269,6 @@
     "Everytime a new LSTM layer is added when updating the `num_lstm_layers` parameter is changed, 12 new metrics will be logged which correspond to mean, std and norm that are calculated. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize model, loss function, and optimizer\n",
-    "\n",
-    "params = {\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 3, # TODO - remove\n",
-    "    \"sequence_length\": 5, # TODO - remove\n",
-    "    \"learning_rate\": 0.001,\n",
-    "    \"epochs\": 5, \n",
-    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
-    "    \"input_features\": 256,\n",
-    "    \"n_classes\": 10, # TODO - remove\n",
-    "    \"input_size\": 28 * 28, # TODO - remove\n",
-    "    \"embed_size\": 1000,\n",
-    "    \"hidden_size\": 256, # hidden size for the LSTM\n",
-    "    \"dropout_prob\": 0.3,\n",
-    "    \"vocab_size\": vocab_size,\n",
-    "    \"num_lstm_layers\": 3\n",
-    "}"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 218,

From fbfb0dad99af39edbfbfefa9e968d2799c4181e5 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 11:16:52 +0100
Subject: [PATCH 037/125] refactor: move data downloading section

---
 .../pytorch_text_model_debugging.ipynb        | 56 +++++++++----------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 9de1dc2..f1d90bd 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "### Introduction\n",
     "\n",
-    "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. \n",
+    "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. This example is designed to be used as a code recipe for you to re-use sections with your own code to edit to adapt to your own model training needs. \n",
     "\n",
     "This guide will show you how to:\n",
     "- Initialize the Neptune Run object and log configuration parameters\n",
@@ -86,6 +86,33 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download or use next token prediction dataset\n",
+    "The dataset used in this example is taken from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset). In this example, you can increase the size of the dataset to test the logging capabilities of Neptune, but note that increasing the dataset size will increase the time taken for the full dataset to download. The current setup only downloads the first 10 parquet files from the Hugging Face public dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For the example, download a random subset of 10% of the original dataset\n",
+    "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
+    "data_files = {\"train\": base_url + \"train-0000*-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
+    "              \"validation\": base_url + \"validation-00000-of-00001.parquet\"} #doanload the complete validation dataset\n",
+    "\n",
+    "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
+    "train_subset = data_subset[\"train\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
+    "validation_subset = data_subset[\"validation\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
+    "\n",
+    "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
+    "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -208,33 +235,6 @@
     "        self.gradients = {}\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download or use next token prediction dataset\n",
-    "The dataset used in this example is taken from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset). In this example, you can increase the size of the dataset to test the logging capabilities of Neptyune, but note that increasing the dataset size will increase the time taken for the full dataset to download."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# For the example, download a random subset of 10% of the original dataset\n",
-    "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
-    "data_files = {\"train\": base_url + \"train-0000*-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
-    "              \"validation\": base_url + \"validation-00000-of-00001.parquet\"} #doanload the complete validation dataset\n",
-    "\n",
-    "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
-    "train_subset = data_subset[\"train\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
-    "validation_subset = data_subset[\"validation\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
-    "\n",
-    "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
-    "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 84149d1f97ba4abc3abb6929b8ed4c26c0329581 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 11:19:07 +0100
Subject: [PATCH 038/125] refactor: added evaluate function to model
 initialization cell

---
 .../pytorch_text_model_debugging.ipynb        | 49 ++++++++-----------
 1 file changed, 21 insertions(+), 28 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index f1d90bd..cb9dc22 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +232,26 @@
     "        return self.gradients\n",
     "    \n",
     "    def clear_gradients(self):\n",
-    "        self.gradients = {}\n"
+    "        self.gradients = {}\n",
+    "\n",
+    "# Function to evaluate the model after each epoch/step\n",
+    "def evaluate(model, val_dataloader, criterion, device):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    total_loss = 0\n",
+    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
+    "        for batch in val_dataloader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "\n",
+    "            # Forward pass for validation\n",
+    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
+    "            \n",
+    "            # Calculate the loss\n",
+    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "    avg_val_loss = total_loss / len(val_dataloader)\n",
+    "    return avg_val_loss"
    ]
   },
   {
@@ -302,32 +321,6 @@
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate(model, val_dataloader, criterion, device):\n",
-    "    model.eval()  # Set the model to evaluation mode\n",
-    "    total_loss = 0\n",
-    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
-    "        for batch in val_dataloader:\n",
-    "            input_ids = batch['input_ids'].to(device)\n",
-    "            labels = batch['labels'].to(device)\n",
-    "\n",
-    "            # Forward pass for validation\n",
-    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
-    "            \n",
-    "            # Calculate the loss\n",
-    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "            total_loss += loss.item()\n",
-    "\n",
-    "    avg_val_loss = total_loss / len(val_dataloader)\n",
-    "    return avg_val_loss\n",
-    "        "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 219,

From 2d69e01f46efdefbe79d27f6918f1282e7acafde Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 11:21:21 +0100
Subject: [PATCH 039/125] chore: update introduction for the model architecture
 and helper functions

---
 .../pytorch/pytorch_text_model_debugging.ipynb            | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index cb9dc22..286db6f 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -174,6 +174,14 @@
     "  - **How to capture**: Visualize feature maps after convolutional layers using libraries like matplotlib.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define PyTorch Model Architecture and Helper functions\n",
+    "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use a mebedding layer, an LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 90e05396eda8d300b7530972ca076c0f4f9a610d Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 11:28:07 +0100
Subject: [PATCH 040/125] fix: update input for vocab_size

---
 .../pytorch/pytorch_text_model_debugging.ipynb              | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 286db6f..9c5d7ae 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -243,7 +243,7 @@
     "        self.gradients = {}\n",
     "\n",
     "# Function to evaluate the model after each epoch/step\n",
-    "def evaluate(model, val_dataloader, criterion, device):\n",
+    "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
     "    model.eval()  # Set the model to evaluation mode\n",
     "    total_loss = 0\n",
     "    with torch.no_grad():  # Disable gradient calculation for validation\n",
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -398,7 +398,7 @@
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
     "\n",
     "        if step_counter % 5 == 0: # Do not need to log validation at every step, although we can\n",
-    "            val_loss = evaluate(model, val_dataloader, criterion, device)\n",
+    "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",
     "            print(f\"Step {step_counter}, Val_loss: {val_loss}\")\n",
     "\n",
     "            # Track activations\n",

From 46e1f37c592df442878d9e1da1192d0a7995e3d7 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 11:29:17 +0100
Subject: [PATCH 041/125] refactor: add the vocab_size calculation to the data
 formatting section

---
 .../pytorch_text_model_debugging.ipynb        | 41 ++++++-------------
 1 file changed, 13 insertions(+), 28 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 9c5d7ae..c9beb47 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -110,7 +110,19 @@
     "validation_subset = data_subset[\"validation\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
     "\n",
     "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
-    "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)"
+    "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)\n",
+    "\n",
+    "# Determine the vocab size of the dataset\n",
+    "# Flatten the list of tokenized sentences into one long list of token IDs\n",
+    "all_tokens = [token for sentence in train_subset[\"input_ids\"] for token in sentence]\n",
+    "\n",
+    "# Get unique token IDs\n",
+    "unique_tokens = set(all_tokens)\n",
+    "\n",
+    "# Vocab size is the number of unique tokens\n",
+    "vocab_size = max(unique_tokens) + 1 # Add 1 since token ID's start from zero\n",
+    "params[\"vocab_size\"] = vocab_size\n",
+    "print(f\"Vocabulary size: {vocab_size}\")"
    ]
   },
   {
@@ -262,33 +274,6 @@
     "    return avg_val_loss"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vocabulary size: 128257\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Determine the vocab size of the dataset\n",
-    "# Flatten the list of tokenized sentences into one long list of token IDs\n",
-    "all_tokens = [token for sentence in train_subset[\"input_ids\"] for token in sentence]\n",
-    "\n",
-    "# Get unique token IDs\n",
-    "unique_tokens = set(all_tokens)\n",
-    "\n",
-    "# Vocab size is the number of unique tokens\n",
-    "vocab_size = max(unique_tokens) + 1 # Add 1 since token ID's start from zero\n",
-    "params[\"vocab_size\"] = vocab_size\n",
-    "print(f\"Vocabulary size: {vocab_size}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

From ca1b52d3af050183ec51a25d8669e67d2d434a2b Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 18:11:27 +0100
Subject: [PATCH 042/125] fix: refactor data loading process from HF

---
 .../pytorch_text_model_debugging.ipynb        | 235 +++++++++++++++---
 1 file changed, 202 insertions(+), 33 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index c9beb47..ed05dd0 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -39,9 +39,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -66,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,31 +105,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatasetDict({\n",
+      "    train: Dataset({\n",
+      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
+      "        num_rows: 81926\n",
+      "    })\n",
+      "    validation: Dataset({\n",
+      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
+      "        num_rows: 9347\n",
+      "    })\n",
+      "})\n",
+      "DatasetDict({\n",
+      "    train: Dataset({\n",
+      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
+      "        num_rows: 8412\n",
+      "    })\n",
+      "    test: Dataset({\n",
+      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
+      "        num_rows: 935\n",
+      "    })\n",
+      "})\n"
+     ]
+    }
+   ],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
     "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
-    "data_files = {\"train\": base_url + \"train-0000*-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
+    "data_files = {\"train\": base_url + \"train-00001-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
     "              \"validation\": base_url + \"validation-00000-of-00001.parquet\"} #doanload the complete validation dataset\n",
     "\n",
     "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
-    "train_subset = data_subset[\"train\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
-    "validation_subset = data_subset[\"validation\"].with_format(\"torch\") # HF provides methods to convert datatypes to tensors\n",
+    "# validation_subset = load_dataset(\"parquet\", data_files = {\"validation\": base_url + \"validation-00000-of-00001.parquet\"}, num_proc=4, split=[\"validation[:5%]\"])\n",
+    "validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
+    "print(data_subset)\n",
+    "print(validation_subset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary size: 128257\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_subset = data_subset[\"train\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
+    "validation_subset = data_subset[\"validation\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
     "\n",
     "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
     "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)\n",
     "\n",
     "# Determine the vocab size of the dataset\n",
     "# Flatten the list of tokenized sentences into one long list of token IDs\n",
-    "all_tokens = [token for sentence in train_subset[\"input_ids\"] for token in sentence]\n",
+    "all_tokens = [token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]\n",
     "\n",
     "# Get unique token IDs\n",
-    "unique_tokens = set(all_tokens)\n",
+    "# unique_tokens = set(all_tokens)\n",
     "\n",
     "# Vocab size is the number of unique tokens\n",
-    "vocab_size = max(unique_tokens) + 1 # Add 1 since token ID's start from zero\n",
+    "vocab_size = max(all_tokens) + 1 # Add 1 since token ID's start from zero\n",
     "params[\"vocab_size\"] = vocab_size\n",
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
@@ -274,6 +330,95 @@
     "    return avg_val_loss"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the simple LLM model with LSTM\n",
+    "class SimpleLLM(nn.Module):\n",
+    "    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):\n",
+    "        super(SimpleLLM, self).__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
+    "        self.lstm = nn.LSTM(embed_size, 512, num_layers = num_layers, dropout = 0.3, batch_first=True)\n",
+    "        self.fc1 = nn.Linear(512, hidden_size)\n",
+    "        self.fc2 = nn.Linear(hidden_size, vocab_size)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.embedding(x)\n",
+    "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
+    "        out = self.fc1(lstm_out) # Use the last output from the LSTM\n",
+    "        out = self.fc2(out)\n",
+    "        return out\n",
+    "    \n",
+    "# model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"])\n",
+    "\n",
+    "# A class to manage hooks for activations and gradients\n",
+    "class HookManager:\n",
+    "    def __init__(self, model):\n",
+    "        self.model = model\n",
+    "        self.hooks = []\n",
+    "        self.activations = {}\n",
+    "        self.gradients = {}\n",
+    "\n",
+    "    # Function to save activations\n",
+    "    def save_activation(self, name):\n",
+    "        def hook(module, input, output):\n",
+    "            self.activations[name] = output\n",
+    "        return hook\n",
+    "\n",
+    "    # Function to save gradients (registering hooks for the model parameters)\n",
+    "    def save_gradient(self, name):\n",
+    "        def hook(module, grad_input, grad_output):\n",
+    "            self.gradients[name] = grad_output[0]\n",
+    "        return hook\n",
+    "\n",
+    "    # Function to register hooks for activations and gradients\n",
+    "    def register_hooks(self):\n",
+    "        # Register forward hooks for activations\n",
+    "        self.hooks.append(self.model.lstm.register_forward_hook(self.save_activation(\"lstm\")))\n",
+    "        self.hooks.append(self.model.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
+    "        self.hooks.append(self.model.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
+    "\n",
+    "        # Register backward hooks for gradients\n",
+    "        for name, module in self.model.named_modules():\n",
+    "            if isinstance(module, (nn.LSTM, nn.Linear)): # You can add more layer types here\n",
+    "                self.hooks.append(module.register_full_backward_hook(self.save_gradient(name)))\n",
+    "\n",
+    "    # Function to clear activations and gradients after use\n",
+    "    def clear(self):\n",
+    "        self.activations = {}\n",
+    "        self.gradients = {}\n",
+    "\n",
+    "    # Function to get activations\n",
+    "    def get_activations(self):\n",
+    "        return self.activations\n",
+    "\n",
+    "    # Function to get gradients\n",
+    "    def get_gradients(self):\n",
+    "        return self.gradients\n",
+    "\n",
+    "# Function to evaluate the model after each epoch/step\n",
+    "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    total_loss = 0\n",
+    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
+    "        for batch in val_dataloader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "\n",
+    "            # Forward pass for validation\n",
+    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
+    "            \n",
+    "            # Calculate the loss\n",
+    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "    avg_val_loss = total_loss / len(val_dataloader)\n",
+    "    return avg_val_loss"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -283,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,11 +446,9 @@
     "        \"config/learning_rate\": params[\"learning_rate\"],\n",
     "        \"config/optimizer\": params[\"optimizer\"],\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
-    "        \"config/sequece_length\": params[\"sequence_length\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/input_size\": params[\"input_size\"],\n",
     "        \"config/num_lstm_layers\" : params[\"num_lstm_layers\"],\n",
-    "        \"data/vocab_size\": params[\"vocab_size\"],\n",
+    "        # \"data/vocab_size\": params[\"vocab_size\"],\n",
     "        \"data/embed_size\": params[\"embed_size\"]\n",
     "    }\n",
     ")\n",
@@ -316,32 +459,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1830: FutureWarning: Using non-full backward hooks on a Module that does not return a single Tensor or a tuple of Tensors is deprecated and will be removed in future versions. This hook will be missing some of the grad_output. Please use register_full_backward_hook to get the documented behavior.\n",
+      "  self._maybe_warn_non_full_backward_hook(args, result, grad_fn)\n",
+      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1830: FutureWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
+      "  self._maybe_warn_non_full_backward_hook(args, result, grad_fn)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 1 / 102408, Loss: 11.76780891418457\n",
-      "Step 2 / 102408, Loss: 11.754159927368164\n"
+      "Step 1 / 10241, Loss: 11.76430606842041\n",
+      "Step 2 / 10241, Loss: 11.756403923034668\n",
+      "Step 3 / 10241, Loss: 11.73224925994873\n",
+      "Step 4 / 10241, Loss: 11.687955856323242\n",
+      "Step 5 / 10241, Loss: 11.431197166442871\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
+     "ename": "KeyError",
+     "evalue": "3",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[219], line 32\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[0;32m     31\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 32\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     34\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:476\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    474\u001b[0m         denom \u001b[38;5;241m=\u001b[39m (max_exp_avg_sqs[i]\u001b[38;5;241m.\u001b[39msqrt() \u001b[38;5;241m/\u001b[39m bias_correction2_sqrt)\u001b[38;5;241m.\u001b[39madd_(eps)\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 476\u001b[0m         denom \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbias_correction2_sqrt\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_\u001b[49m\u001b[43m(\u001b[49m\u001b[43meps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    478\u001b[0m     param\u001b[38;5;241m.\u001b[39maddcdiv_(exp_avg, denom, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39mstep_size)\n\u001b[0;32m    480\u001b[0m \u001b[38;5;66;03m# Lastly, switch back to complex view\u001b[39;00m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[37], line 42\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_counter \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Do not need to log validation at every step, although we can\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvocab_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     43\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Val_loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[30], line 69\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, val_dataloader, criterion, device, vocab_size)\u001b[0m\n\u001b[0;32m     67\u001b[0m total_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():  \u001b[38;5;66;03m# Disable gradient calculation for validation\u001b[39;00m\n\u001b[1;32m---> 69\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m     70\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     71\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabels\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
+      "\u001b[1;31mKeyError\u001b[0m: 3"
      ]
     }
    ],
@@ -353,6 +507,9 @@
     "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
     "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n",
     "\n",
+    "hook_manager = HookManager(model)\n",
+    "hook_manager.register_hooks()\n",
+    "\n",
     "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
     "model.to(device)\n",
     "step_counter = 0\n",
@@ -378,6 +535,7 @@
     "        # Backward pass and optimization\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
+    "\n",
     "        \n",
     "        total_loss += loss.item()\n",
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
@@ -391,9 +549,11 @@
     "                debug_metrics[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
     "                debug_metrics[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
     "\n",
-    "            # Track gradient norm\n",
-    "            # for layer, gradient in model.get_gradients().items():\n",
-    "             #   grad_norms[f\"grad_norm/{name}\"] = gradient.norm().item()\n",
+    "            # Track gradients with hooks\n",
+    "            gradients = hook_manager.get_gradients()\n",
+    "            for layer, gradient in gradients.items():\n",
+    "               # debug_metrics[f\"grad_hooks/{name}\"] = gradient.shape()\n",
+    "               print(f\"Gradients for {name}: {gradient.shape}\")\n",
     "\n",
     "            # Track gradients per layer at each epoch\n",
     "            for name, param in model.named_parameters():\n",
@@ -420,9 +580,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "neptune:INFO: Waiting for all operations to be processed\n",
+      "neptune:WARNING: No timeout specified. Waiting indefinitely\n",
+      "neptune:INFO: All operations were processed\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",

From 836c452ddfcd0616d79bccb555205e15f3bf5834 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 10 Mar 2025 20:12:39 +0100
Subject: [PATCH 043/125] fix: update validation data to use test subset from
 HF and comment out activation tracking

---
 .../pytorch_text_model_debugging.ipynb        | 81 +++++++++----------
 1 file changed, 36 insertions(+), 45 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index ed05dd0..1a91f7a 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -163,7 +163,7 @@
    ],
    "source": [
     "train_subset = data_subset[\"train\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
-    "validation_subset = data_subset[\"validation\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
+    "validation_subset = validation_subset[\"test\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
     "\n",
     "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
     "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)\n",
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,43 +459,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1830: FutureWarning: Using non-full backward hooks on a Module that does not return a single Tensor or a tuple of Tensors is deprecated and will be removed in future versions. This hook will be missing some of the grad_output. Please use register_full_backward_hook to get the documented behavior.\n",
-      "  self._maybe_warn_non_full_backward_hook(args, result, grad_fn)\n",
-      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1830: FutureWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
-      "  self._maybe_warn_non_full_backward_hook(args, result, grad_fn)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 1 / 10241, Loss: 11.76430606842041\n",
-      "Step 2 / 10241, Loss: 11.756403923034668\n",
-      "Step 3 / 10241, Loss: 11.73224925994873\n",
-      "Step 4 / 10241, Loss: 11.687955856323242\n",
-      "Step 5 / 10241, Loss: 11.431197166442871\n"
+      "Step 1 / 10241, Loss: 11.765188217163086\n",
+      "Step 2 / 10241, Loss: 11.752411842346191\n",
+      "Step 3 / 10241, Loss: 11.724653244018555\n",
+      "Step 4 / 10241, Loss: 11.64705753326416\n",
+      "Step 5 / 10241, Loss: 11.223550796508789\n",
+      "Step 5, Val_loss: 10.67489557999831\n",
+      "Gradients for fc2: 1.2764140652500025e-16\n",
+      "Gradients for fc1: 3.7073445469104627e-07\n",
+      "Gradients for lstm: 8.93705909277287e-09\n",
+      "Step 6 / 10241, Loss: 10.669904708862305\n",
+      "Step 7 / 10241, Loss: 10.110128402709961\n",
+      "Step 8 / 10241, Loss: 9.748831748962402\n"
      ]
     },
     {
-     "ename": "KeyError",
-     "evalue": "3",
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[37], line 42\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_counter \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Do not need to log validation at every step, although we can\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvocab_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     43\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Val_loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[30], line 69\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, val_dataloader, criterion, device, vocab_size)\u001b[0m\n\u001b[0;32m     67\u001b[0m total_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():  \u001b[38;5;66;03m# Disable gradient calculation for validation\u001b[39;00m\n\u001b[1;32m---> 69\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m     70\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     71\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabels\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
-      "\u001b[1;31mKeyError\u001b[0m: 3"
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[32], line 35\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[0;32m     34\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 35\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     38\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:425\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m    423\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mlerp_(grad, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m device_beta1)\n\u001b[1;32m--> 425\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maddcmul_(grad, grad\u001b[38;5;241m.\u001b[39mconj(), value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta2)\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[0;32m    428\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -545,15 +544,16 @@
     "            print(f\"Step {step_counter}, Val_loss: {val_loss}\")\n",
     "\n",
     "            # Track activations\n",
-    "            for name, activation in model.get_activations().items():\n",
-    "                debug_metrics[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
-    "                debug_metrics[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
+    "            # for name, activation in model.get_activations().items():\n",
+    "            #    debug_metrics[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
+    "            #   debug_metrics[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
     "\n",
     "            # Track gradients with hooks\n",
     "            gradients = hook_manager.get_gradients()\n",
     "            for layer, gradient in gradients.items():\n",
     "               # debug_metrics[f\"grad_hooks/{name}\"] = gradient.shape()\n",
-    "               print(f\"Gradients for {name}: {gradient.shape}\")\n",
+    "               print(f\"Gradients for {layer}: {gradient.mean().item()}\")\n",
+    "               # print(f\"Gradients for {layer}: {gradient.grad.mean()}\")\n",
     "\n",
     "            # Track gradients per layer at each epoch\n",
     "            for name, param in model.named_parameters():\n",
@@ -580,18 +580,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "neptune:INFO: Waiting for all operations to be processed\n",
-      "neptune:WARNING: No timeout specified. Waiting indefinitely\n",
-      "neptune:INFO: All operations were processed\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",

From 814d56f8c7f663a450a34d1febf09d6c5d30738b Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 11 Mar 2025 11:39:12 +0100
Subject: [PATCH 044/125] refactor: create a class to manage hooks for tracking
 gradients and activations in the model

---
 .../pytorch/pytorch_text_model_debugging.ipynb | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 1a91f7a..515e1f0 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -377,9 +377,8 @@
     "    # Function to register hooks for activations and gradients\n",
     "    def register_hooks(self):\n",
     "        # Register forward hooks for activations\n",
-    "        self.hooks.append(self.model.lstm.register_forward_hook(self.save_activation(\"lstm\")))\n",
-    "        self.hooks.append(self.model.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
-    "        self.hooks.append(self.model.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
+    "        for name, module in self.model.named_modules():\n",
+    "            self.hooks.append(module.register_forward_hook(self.save_activation(name)))\n",
     "\n",
     "        # Register backward hooks for gradients\n",
     "        for name, module in self.model.named_modules():\n",
@@ -459,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -544,16 +543,19 @@
     "            print(f\"Step {step_counter}, Val_loss: {val_loss}\")\n",
     "\n",
     "            # Track activations\n",
-    "            # for name, activation in model.get_activations().items():\n",
+    "            activations = hook_manager.get_activations()\n",
+    "            for layer, activation in activations.items():\n",
+    "                print(f\"Activations for {layer}: {activation[0].mean().item()}\")\n",
     "            #    debug_metrics[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
     "            #   debug_metrics[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
     "\n",
     "            # Track gradients with hooks\n",
     "            gradients = hook_manager.get_gradients()\n",
     "            for layer, gradient in gradients.items():\n",
-    "               # debug_metrics[f\"grad_hooks/{name}\"] = gradient.shape()\n",
-    "               print(f\"Gradients for {layer}: {gradient.mean().item()}\")\n",
-    "               # print(f\"Gradients for {layer}: {gradient.grad.mean()}\")\n",
+    "               debug_metrics[f\"grad_hooks/{layer}\"] = gradient.mean().item()\n",
+    "               print(f\"Gradients for {layer}: {gradient.mean().item()}\") # You can replace to use mean(), sum(), max() or min()\n",
+    "               # simplified_gradient = gradient.mean(dim=(0, 1))\n",
+    "               # print(f\"Summed Gradient for {layer}: {simplified_gradient}\")\n",
     "\n",
     "            # Track gradients per layer at each epoch\n",
     "            for name, param in model.named_parameters():\n",

From 31968e9bcef0f9a64cf959f426783fb92a4056f3 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 11 Mar 2025 14:50:03 +0100
Subject: [PATCH 045/125] fix: gradients logging to Neptune

---
 .../pytorch_text_model_debugging.ipynb        | 382 +-----------------
 1 file changed, 14 insertions(+), 368 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 515e1f0..e17f807 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -458,45 +458,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 1 / 10241, Loss: 11.765188217163086\n",
-      "Step 2 / 10241, Loss: 11.752411842346191\n",
-      "Step 3 / 10241, Loss: 11.724653244018555\n",
-      "Step 4 / 10241, Loss: 11.64705753326416\n",
-      "Step 5 / 10241, Loss: 11.223550796508789\n",
-      "Step 5, Val_loss: 10.67489557999831\n",
-      "Gradients for fc2: 1.2764140652500025e-16\n",
-      "Gradients for fc1: 3.7073445469104627e-07\n",
-      "Gradients for lstm: 8.93705909277287e-09\n",
-      "Step 6 / 10241, Loss: 10.669904708862305\n",
-      "Step 7 / 10241, Loss: 10.110128402709961\n",
-      "Step 8 / 10241, Loss: 9.748831748962402\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[32], line 35\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[0;32m     34\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m---> 35\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     38\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    489\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    490\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    491\u001b[0m             )\n\u001b[1;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m     90\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[1;32m---> 91\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:244\u001b[0m, in \u001b[0;36mAdam.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    232\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    234\u001b[0m     has_complex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[0;32m    235\u001b[0m         group,\n\u001b[0;32m    236\u001b[0m         params_with_grad,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    241\u001b[0m         state_steps,\n\u001b[0;32m    242\u001b[0m     )\n\u001b[1;32m--> 244\u001b[0m     \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    249\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamsgrad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\optimizer.py:154\u001b[0m, in \u001b[0;36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:876\u001b[0m, in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    874\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adam\n\u001b[1;32m--> 876\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    895\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\optim\\adam.py:425\u001b[0m, in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[0;32m    423\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mlerp_(grad, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m device_beta1)\n\u001b[1;32m--> 425\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maddcmul_(grad, grad\u001b[38;5;241m.\u001b[39mconj(), value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta2)\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[0;32m    428\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "debug_metrics = {}\n",
     "\n",
@@ -518,6 +482,7 @@
     "    for batch in train_dataloader:\n",
     "        model.train()\n",
     "        step_counter += 1\n",
+    "        hook_manager.clear()\n",
     "\n",
     "        input_ids = batch['input_ids'].to(device)\n",
     "        labels = batch['labels'].to(device)\n",
@@ -546,8 +511,8 @@
     "            activations = hook_manager.get_activations()\n",
     "            for layer, activation in activations.items():\n",
     "                print(f\"Activations for {layer}: {activation[0].mean().item()}\")\n",
-    "            #    debug_metrics[f\"layers/layer_{name}/activation_mean\"] = activation[0].mean().item()\n",
-    "            #   debug_metrics[f\"layers/layer_{name}/activation_std\"] = activation[0].std().item()\n",
+    "                debug_metrics[f\"layers/layer_{layer}/activation_mean\"] = activation[0].mean().item()\n",
+    "                debug_metrics[f\"layers/layer_{layer}/activation_std\"] = activation[0].std().item()\n",
     "\n",
     "            # Track gradients with hooks\n",
     "            gradients = hook_manager.get_gradients()\n",
@@ -602,338 +567,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Activation for lstm layer:  (tensor([[[-1.1885e-01,  7.4334e-02, -1.1457e-01,  ...,  6.1157e-02,\n",
-      "           3.4403e-02,  1.4354e-01],\n",
-      "         [ 2.1115e-02, -9.9912e-02, -1.5829e-01,  ...,  6.8039e-02,\n",
-      "           5.0781e-02,  1.2131e-01],\n",
-      "         [-5.1509e-02, -2.8223e-02, -1.2977e-01,  ...,  5.5352e-02,\n",
-      "           1.3167e-01, -9.2887e-02],\n",
-      "         [ 1.4676e-01, -5.5235e-02, -2.3041e-01,  ...,  1.3869e-01,\n",
-      "           6.1838e-02, -1.1159e-01],\n",
-      "         [ 9.8354e-03,  5.3392e-02, -3.0002e-02,  ...,  1.5939e-01,\n",
-      "           4.8001e-02, -1.6666e-01]],\n",
-      "\n",
-      "        [[-6.9048e-03, -1.4166e-01,  1.8845e-03,  ...,  1.3928e-01,\n",
-      "          -2.7910e-01,  1.3777e-01],\n",
-      "         [-7.3346e-02, -1.0498e-01,  3.9164e-02,  ...,  1.7106e-01,\n",
-      "          -8.2462e-02, -5.4011e-02],\n",
-      "         [ 1.3681e-01, -5.4486e-02, -7.6502e-02,  ...,  1.2276e-01,\n",
-      "           5.4242e-03, -1.5655e-01],\n",
-      "         [ 1.3117e-01, -1.4139e-01, -2.0100e-01,  ...,  1.5732e-01,\n",
-      "          -1.0666e-01, -8.7156e-02],\n",
-      "         [ 9.4591e-02, -5.7793e-02, -1.3404e-01,  ...,  9.6900e-02,\n",
-      "           4.9515e-03, -7.9186e-03]],\n",
-      "\n",
-      "        [[-9.0859e-02,  5.5639e-03, -1.4513e-01,  ..., -7.1660e-02,\n",
-      "           1.8301e-01,  7.4892e-03],\n",
-      "         [ 9.2305e-02,  1.1849e-01, -1.5432e-01,  ..., -3.9754e-02,\n",
-      "           2.1528e-01, -6.6336e-02],\n",
-      "         [-4.3912e-02,  7.2306e-03, -1.6964e-01,  ...,  3.7397e-02,\n",
-      "           1.3741e-01, -7.8224e-03],\n",
-      "         [-2.3771e-03, -1.7121e-01, -1.9225e-01,  ...,  2.1824e-02,\n",
-      "           3.4948e-02, -1.7641e-04],\n",
-      "         [ 1.3603e-01, -6.7352e-02, -2.7966e-01,  ...,  1.0816e-01,\n",
-      "          -9.4453e-02,  1.8542e-01]]], grad_fn=<TransposeBackward0>), (tensor([[[ 9.8354e-03,  5.3392e-02, -3.0002e-02,  7.2875e-03, -1.4585e-01,\n",
-      "           1.2482e-02,  6.9810e-02, -9.4523e-02,  8.2014e-02,  1.4978e-02,\n",
-      "          -2.2127e-01, -8.6931e-02, -1.7447e-02, -9.0253e-02, -5.8715e-02,\n",
-      "          -9.7126e-02, -1.4640e-02,  6.9750e-02, -4.0392e-02, -6.4946e-03,\n",
-      "           6.3030e-02, -2.8948e-03,  2.3277e-02,  2.1406e-01, -7.2021e-02,\n",
-      "          -8.5262e-02,  2.1904e-01,  5.9262e-02,  8.1430e-02, -5.0410e-02,\n",
-      "           4.7898e-02, -1.7856e-02, -9.6553e-02,  1.2176e-01,  1.3534e-01,\n",
-      "           9.9480e-02, -6.4945e-02, -8.7489e-02,  1.9893e-01, -1.1475e-02,\n",
-      "          -1.1274e-01,  1.4311e-01,  1.2569e-01,  9.3439e-03,  7.4138e-02,\n",
-      "          -4.6937e-02,  1.2031e-01,  3.7596e-02, -1.1246e-01, -1.1382e-01,\n",
-      "          -4.6571e-02, -6.7600e-02, -1.0755e-01,  1.3048e-01, -1.8727e-01,\n",
-      "          -1.5687e-01,  1.3892e-01, -2.1552e-01, -5.3767e-02,  1.5782e-02,\n",
-      "           1.7370e-01,  3.1182e-02,  1.0335e-01,  2.5303e-02, -1.8224e-02,\n",
-      "          -6.4270e-03, -1.8351e-02, -7.7923e-02,  7.3565e-03,  1.0760e-01,\n",
-      "           2.1875e-02, -4.9347e-02,  7.0013e-02, -4.0485e-02,  8.5161e-03,\n",
-      "          -1.6069e-01, -7.3729e-02, -1.1683e-01,  1.1353e-01,  9.2289e-02,\n",
-      "          -2.1123e-01, -2.1108e-02, -3.3057e-02,  9.3739e-02, -7.4621e-02,\n",
-      "          -3.0965e-02,  3.7901e-02,  9.5562e-02,  5.1191e-02, -8.1342e-02,\n",
-      "           1.9611e-02, -1.1581e-01, -2.1830e-01,  1.9827e-02, -3.9519e-02,\n",
-      "           1.1952e-01,  6.1484e-02,  1.3787e-01,  4.1152e-02, -6.1684e-02,\n",
-      "          -5.6594e-02, -3.1420e-02, -2.2474e-02, -6.5657e-02,  1.4875e-01,\n",
-      "          -9.1052e-02,  7.7182e-02,  4.8757e-02,  1.4903e-01, -1.3527e-03,\n",
-      "           2.7246e-01,  1.2794e-01, -1.7041e-01, -7.6142e-02,  1.3974e-01,\n",
-      "           2.4618e-02, -6.9025e-02, -9.4079e-02, -1.4909e-03,  5.9612e-02,\n",
-      "          -4.4725e-03, -6.7179e-02, -6.1797e-02,  1.6559e-01, -8.0163e-02,\n",
-      "          -2.2909e-01,  6.6350e-02,  1.1361e-02,  8.4108e-02, -1.1556e-01,\n",
-      "           6.1252e-02,  8.3287e-02, -8.7894e-02, -1.0695e-01, -5.3925e-02,\n",
-      "           7.1153e-02,  6.9016e-02, -1.4300e-02, -5.9164e-02,  3.7659e-02,\n",
-      "           1.0063e-01,  1.0336e-01, -1.9477e-02,  3.5742e-02, -3.3210e-02,\n",
-      "          -1.6847e-03, -9.3591e-02,  1.4821e-01,  8.3384e-02,  8.9724e-02,\n",
-      "          -1.2488e-01, -1.8335e-01, -1.4115e-01,  1.3415e-01,  1.4753e-01,\n",
-      "           4.4653e-02,  1.9245e-02,  3.8093e-02,  1.0158e-01, -4.3646e-02,\n",
-      "          -7.2102e-02,  1.9797e-02,  3.9576e-02,  2.4157e-02, -1.0626e-01,\n",
-      "          -2.5799e-01,  3.5465e-02, -1.9632e-01,  1.2119e-01,  4.7838e-02,\n",
-      "           1.0147e-02, -3.0875e-03, -4.0234e-03,  3.6550e-02,  2.3824e-02,\n",
-      "          -4.8517e-02, -8.0195e-02,  3.5668e-02, -3.0944e-02,  9.3462e-02,\n",
-      "           1.5813e-01, -6.5189e-02,  7.8902e-03,  1.2792e-01,  1.2325e-01,\n",
-      "          -7.1469e-02, -6.0525e-03,  4.5219e-02,  2.8174e-02,  2.1492e-01,\n",
-      "          -5.7059e-03, -5.3889e-03, -1.9786e-01, -2.9867e-02, -1.3545e-01,\n",
-      "          -6.7783e-02, -9.1990e-02,  1.7466e-02, -8.8718e-02,  3.5585e-02,\n",
-      "          -4.7237e-02,  4.2510e-02,  1.7164e-01,  6.5421e-02,  7.3680e-02,\n",
-      "          -4.8147e-02, -2.3811e-02, -1.4102e-01, -1.7029e-02,  1.5386e-01,\n",
-      "          -1.0342e-01,  2.5038e-03,  1.0144e-01,  1.2401e-02,  5.6638e-03,\n",
-      "           2.2166e-02,  1.2892e-02, -2.7320e-02,  6.6498e-02, -7.0570e-02,\n",
-      "          -1.1974e-02,  1.3094e-01, -1.1246e-01,  1.0381e-01, -7.8150e-02,\n",
-      "          -3.4429e-02,  1.4121e-01, -1.1700e-01, -1.3806e-01,  8.8438e-02,\n",
-      "          -7.9613e-02,  7.0094e-02, -6.7182e-02,  3.2045e-02, -2.9587e-02,\n",
-      "           1.8854e-01, -2.0000e-02, -1.3733e-01, -1.7716e-01,  1.5292e-02,\n",
-      "          -4.1364e-03,  2.0001e-02,  1.2971e-01,  1.7419e-01,  5.9393e-02,\n",
-      "           3.4792e-02, -2.2356e-01,  7.1222e-02, -6.3988e-02, -3.1165e-02,\n",
-      "          -5.1605e-02, -3.3989e-02,  1.2333e-02,  1.5939e-01,  4.8001e-02,\n",
-      "          -1.6666e-01],\n",
-      "         [ 9.4591e-02, -5.7793e-02, -1.3404e-01,  2.1016e-01, -1.3421e-01,\n",
-      "          -1.1376e-01,  1.1109e-01,  1.0350e-01, -2.2410e-02, -4.0062e-02,\n",
-      "           3.2718e-02, -6.8034e-02,  5.5625e-02,  1.5184e-02, -9.1550e-04,\n",
-      "          -6.3774e-02, -6.9557e-02,  1.0805e-01,  4.2219e-02, -6.3613e-02,\n",
-      "          -9.4216e-02,  6.2111e-02, -5.3061e-02, -1.5292e-01,  1.2790e-01,\n",
-      "          -7.5749e-02,  1.4717e-01,  6.3722e-02,  8.9517e-02,  6.4354e-02,\n",
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-      "          -0.0483, -0.2983, -0.2004, -0.3418,  0.1489,  0.1367, -0.1541,\n",
-      "          -0.0908, -0.1317,  0.1192,  0.4313, -0.0851, -0.1807,  0.1204,\n",
-      "          -0.1759, -0.2699,  0.0323, -0.4391, -0.0156,  0.1651, -0.0330,\n",
-      "          -0.0990,  0.1090,  0.1267,  0.2578,  0.0501, -0.1733, -0.1170,\n",
-      "          -0.1403, -0.4787,  0.1272, -0.0632,  0.0988,  0.3264,  0.1055,\n",
-      "           0.0343,  0.0405, -0.0056,  0.1102, -0.2660, -0.1654,  0.1530,\n",
-      "           0.0636, -0.0223,  0.1924, -0.2318, -0.1286,  0.2206,  0.1257,\n",
-      "           0.0081, -0.2056, -0.0625,  0.2139, -0.3882,  0.6290, -0.0291,\n",
-      "           0.0282, -0.1494,  0.0187, -0.0855, -0.2410, -0.1237,  0.0555,\n",
-      "           0.1439,  0.1793,  0.0084, -0.0185],\n",
-      "         [ 0.3369, -0.1479, -0.5276,  0.1676, -0.1363,  0.0398,  0.1985,\n",
-      "           0.2396, -0.1705,  0.1846,  0.0800,  0.0618, -0.0620,  0.2784,\n",
-      "          -0.2426, -0.0458,  0.1770, -0.1390, -0.0845,  0.1183, -0.1349,\n",
-      "          -0.1174, -0.0712, -0.0334, -0.4726,  0.1418,  0.0468, -0.0561,\n",
-      "          -0.0607,  0.2376,  0.0528, -0.5115, -0.2481,  0.1050, -0.1376,\n",
-      "           0.2348, -0.1902, -0.0503, -0.1183,  0.2031, -0.2646, -0.1380,\n",
-      "          -0.1213, -0.0263,  0.1222,  0.0415,  0.1292,  0.1448, -0.0367,\n",
-      "           0.3030,  0.1181,  0.0593, -0.1332, -0.4023,  0.0568,  0.2586,\n",
-      "          -0.1512,  0.1804,  0.4000, -0.0546,  0.0219,  0.0816, -0.0157,\n",
-      "          -0.0265, -0.1322,  0.0508, -0.2013,  0.0325, -0.2295, -0.0557,\n",
-      "           0.1835, -0.0470,  0.1761,  0.2084, -0.0915, -0.3008,  0.0123,\n",
-      "          -0.1079,  0.0595,  0.1632,  0.1830,  0.1273,  0.1986, -0.0415,\n",
-      "          -0.0714, -0.3162,  0.2042, -0.1503,  0.2874, -0.0081, -0.1331,\n",
-      "          -0.0118, -0.1135,  0.0777, -0.1381,  0.1587,  0.2140, -0.0439,\n",
-      "          -0.3028,  0.1019,  0.0596, -0.1013, -0.0633,  0.1042,  0.0591,\n",
-      "          -0.0395,  0.0779, -0.0082, -0.0133, -0.0734,  0.0098,  0.0712,\n",
-      "           0.1292, -0.3829,  0.0417,  0.2043, -0.1393,  0.2560,  0.1654,\n",
-      "          -0.0879,  0.2017,  0.0713, -0.0830,  0.1072,  0.1062,  0.2560,\n",
-      "          -0.4510,  0.1106,  0.1474,  0.1427, -0.0086,  0.3868,  0.3241,\n",
-      "           0.4367,  0.0656,  0.0621, -0.1387,  0.1025, -0.0375, -0.2811,\n",
-      "          -0.0774, -0.0591, -0.3121,  0.1357,  0.3206,  0.0585, -0.2302,\n",
-      "          -0.0596, -0.2986, -0.0889, -0.0869, -0.1366,  0.0568,  0.0286,\n",
-      "          -0.1777, -0.0427, -0.1456, -0.1354,  0.0482, -0.3316, -0.1904,\n",
-      "          -0.4873, -0.5369, -0.1222,  0.0694, -0.0939, -0.1674,  0.0686,\n",
-      "           0.0854,  0.1132,  0.2842, -0.1667,  0.0033,  0.0907,  0.1604,\n",
-      "          -0.2291, -0.2927,  0.3881,  0.0979,  0.4785,  0.1536, -0.4774,\n",
-      "          -0.2153,  0.1427, -0.1574,  0.1796,  0.3557, -0.0449, -0.0526,\n",
-      "          -0.3581, -0.0234, -0.3326,  0.0049,  0.0933, -0.3071, -0.1452,\n",
-      "          -0.2091, -0.0060, -0.0311, -0.0465,  0.1057,  0.1421, -0.0608,\n",
-      "          -0.1065, -0.0965, -0.1038, -0.3565,  0.0803,  0.0465,  0.2869,\n",
-      "          -0.0856,  0.3659,  0.1754, -0.3457,  0.0169, -0.2568,  0.2813,\n",
-      "          -0.0543,  0.0783,  0.1306, -0.2800, -0.1639,  0.2811,  0.0763,\n",
-      "          -0.1103, -0.4329,  0.1929, -0.0220, -0.0827,  0.2655,  0.0324,\n",
-      "           0.0027,  0.0717,  0.2442,  0.0713,  0.1201,  0.2816, -0.0332,\n",
-      "           0.2074, -0.1304, -0.0855, -0.3259, -0.0834,  0.3056,  0.0815,\n",
-      "          -0.1666, -0.1157, -0.0828, -0.2060,  0.0282, -0.0599,  0.0598,\n",
-      "           0.0145,  0.2333, -0.1539,  0.4261]]], grad_fn=<StackBackward0>)))\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gradient for fc layer:  torch.Size([3, 5, 5000])\n"
+     "ename": "KeyError",
+     "evalue": "0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[45], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# After training, you can analyze the activations and gradients\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# Example: Plot the activations of the LSTM layer\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m layer_name, activation \u001b[38;5;241m=\u001b[39m \u001b[43mactivations\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m  \u001b[38;5;66;03m# Assuming lstm activation\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mActivation for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m layer: \u001b[39m\u001b[38;5;124m\"\u001b[39m, activation)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Plotting histogram of activations of the LSTM layer\u001b[39;00m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 0"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [

From a9bad6177dbfa26f90e5406e8dcb5c01d068550c Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 11 Mar 2025 14:56:09 +0100
Subject: [PATCH 046/125] style: remove old model architecture section

---
 .../pytorch_text_model_debugging.ipynb        | 139 +++++++-----------
 1 file changed, 55 insertions(+), 84 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index e17f807..798c936 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -252,87 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the simple LLM model with LSTM\n",
-    "class SimpleLLM(nn.Module):\n",
-    "    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):\n",
-    "        super(SimpleLLM, self).__init__()\n",
-    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, 512, num_layers = num_layers, dropout = 0.3, batch_first=True)\n",
-    "        self.fc1 = nn.Linear(512, hidden_size)\n",
-    "        self.fc2 = nn.Linear(hidden_size, vocab_size)\n",
-    "\n",
-    "        # Initialize activations dictionary to store layer activations\n",
-    "        self.activations = {}\n",
-    "\n",
-    "        ## Registering hooks to track activations\n",
-    "        self.hooks = []\n",
-    "        self.hooks.append(self.lstm.register_forward_hook(self.save_activation(\"lstm\")))\n",
-    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
-    "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
-    "\n",
-    "        ## Registering hooks to track gradients\n",
-    "        #self.hooks.append(self.lstm.register_full_backward_hook(self.save_gradient(\"lstm\")))\n",
-    "        #self.hooks.append(self.fc1.register_full_backward_hook(self.save_gradient(\"fc1\")))\n",
-    "        #self.hooks.append(self.fc2.register_full_backward_hook(self.save_gradient(\"fc2\")))\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        x = self.embedding(x)\n",
-    "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
-    "        out = self.fc1(lstm_out) # Use the last output from the LSTM\n",
-    "        out = self.fc2(out)\n",
-    "        return out\n",
-    "    \n",
-    "    # Function to save activations\n",
-    "    def save_activation(self, name):\n",
-    "        def hook(model, input, output):\n",
-    "            self.activations[name] = output\n",
-    "        return hook\n",
-    "    \n",
-    "    def get_activations(self):\n",
-    "        return self.activations\n",
-    "\n",
-    "    def clear_activations(self):\n",
-    "        self.activations = {}\n",
-    "\n",
-    "    # Function to save gradients TODO: check where gradients should be calculated\n",
-    "    def save_gradient(self, name):\n",
-    "        def hook(module, grad_input, grad_output):\n",
-    "            self.gradients[name] = grad_output[0]  # Save gradient output in the dictionary\n",
-    "        return hook\n",
-    "    \n",
-    "    def get_gradients(self):\n",
-    "        return self.gradients\n",
-    "    \n",
-    "    def clear_gradients(self):\n",
-    "        self.gradients = {}\n",
-    "\n",
-    "# Function to evaluate the model after each epoch/step\n",
-    "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
-    "    model.eval()  # Set the model to evaluation mode\n",
-    "    total_loss = 0\n",
-    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
-    "        for batch in val_dataloader:\n",
-    "            input_ids = batch['input_ids'].to(device)\n",
-    "            labels = batch['labels'].to(device)\n",
-    "\n",
-    "            # Forward pass for validation\n",
-    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
-    "            \n",
-    "            # Calculate the loss\n",
-    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "            total_loss += loss.item()\n",
-    "\n",
-    "    avg_val_loss = total_loss / len(val_dataloader)\n",
-    "    return avg_val_loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +380,58 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 1 / 10241, Loss: 11.760147094726562\n",
+      "Step 2 / 10241, Loss: 11.744263648986816\n",
+      "Step 3 / 10241, Loss: 11.720343589782715\n",
+      "Step 4 / 10241, Loss: 11.64067268371582\n",
+      "Step 5 / 10241, Loss: 11.333736419677734\n",
+      "Step 5, Val_loss: 10.690058871212168\n",
+      "Activations for embedding: -0.0025795602705329657\n",
+      "Activations for lstm: -0.0010381790343672037\n",
+      "Activations for fc1: -0.07045605778694153\n",
+      "Activations for fc2: -0.679652988910675\n",
+      "Activations for : -0.679652988910675\n",
+      "Gradients for fc2: 6.027510937207733e-17\n",
+      "Gradients for fc1: 3.636175804899722e-08\n",
+      "Gradients for lstm: -3.4538189197519387e-08\n",
+      "Step 6 / 10241, Loss: 10.451458930969238\n",
+      "Step 7 / 10241, Loss: 10.109658241271973\n",
+      "Step 8 / 10241, Loss: 10.559635162353516\n",
+      "Step 9 / 10241, Loss: 9.650898933410645\n",
+      "Step 10 / 10241, Loss: 9.617280006408691\n",
+      "Step 10, Val_loss: 9.465246697776339\n",
+      "Activations for embedding: -0.003705302719026804\n",
+      "Activations for lstm: -0.008110555820167065\n",
+      "Activations for fc1: -0.14376381039619446\n",
+      "Activations for fc2: -2.851902961730957\n",
+      "Activations for : -2.851902961730957\n",
+      "Gradients for fc2: -2.2691806192661813e-16\n",
+      "Gradients for fc1: -5.234873583503941e-07\n",
+      "Gradients for lstm: 5.951231685230596e-08\n",
+      "Step 11 / 10241, Loss: 9.610464096069336\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[48], line 35\u001b[0m\n\u001b[0;32m     32\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     36\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     39\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "debug_metrics = {}\n",
     "\n",
@@ -547,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {

From 32d46f15f03e40c174ce00cb57fcf6267597d9af Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 11 Mar 2025 15:09:33 +0100
Subject: [PATCH 047/125] refactor: change attribute names for better
 readability

---
 .../pytorch/pytorch_text_model_debugging.ipynb | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 798c936..113801d 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,24 +481,24 @@
     "            # Track activations\n",
     "            activations = hook_manager.get_activations()\n",
     "            for layer, activation in activations.items():\n",
-    "                print(f\"Activations for {layer}: {activation[0].mean().item()}\")\n",
-    "                debug_metrics[f\"layers/layer_{layer}/activation_mean\"] = activation[0].mean().item()\n",
-    "                debug_metrics[f\"layers/layer_{layer}/activation_std\"] = activation[0].std().item()\n",
+    "                if layer is not None:\n",
+    "                    debug_metrics[f\"debug/activation/{layer}_mean\"] = activation[0].mean().item()\n",
+    "                    debug_metrics[f\"debug/activation/{layer}_std\"] = activation[0].std().item()\n",
     "\n",
     "            # Track gradients with hooks\n",
     "            gradients = hook_manager.get_gradients()\n",
     "            for layer, gradient in gradients.items():\n",
-    "               debug_metrics[f\"grad_hooks/{layer}\"] = gradient.mean().item()\n",
+    "               debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
     "               print(f\"Gradients for {layer}: {gradient.mean().item()}\") # You can replace to use mean(), sum(), max() or min()\n",
     "               # simplified_gradient = gradient.mean(dim=(0, 1))\n",
     "               # print(f\"Summed Gradient for {layer}: {simplified_gradient}\")\n",
     "\n",
     "            # Track gradients per layer at each epoch\n",
-    "            for name, param in model.named_parameters():\n",
+    "            for layer, param in model.named_parameters():\n",
     "                if param is not None:\n",
-    "                    debug_metrics[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
-    "                    debug_metrics[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "                    debug_metrics[f\"layers/layer_{name}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
+    "                    debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
+    "                    debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
+    "                    debug_metrics[f\"debug/parameters/{layer}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
     "\n",
     "            # Output loss for this epoch\n",
     "            run.log_metrics(\n",

From 32a43b1f24caf9f08304b07093320c76000a7948 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 12 Mar 2025 13:05:08 +0100
Subject: [PATCH 048/125] style: update sections and model architecture

---
 .../pytorch_text_model_debugging.ipynb        | 242 +++++++++---------
 1 file changed, 121 insertions(+), 121 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 113801d..75881c7 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -10,14 +10,26 @@
     "\n",
     "### Introduction\n",
     "\n",
-    "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. This example is designed to be used as a code recipe for you to re-use sections with your own code to edit to adapt to your own model training needs. \n",
+    "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. This example is designed to be used as a code recipe for you to re-use sections with your own code to edit or adapt to your own model training needs. \n",
     "\n",
     "This guide will show you how to:\n",
     "- Initialize the Neptune Run object and log configuration parameters\n",
     "- Log standard loss and accuracy metrics to Neptune\n",
-    "- Log debugging metrics during model training such as;\n",
-    "    * Activations per layer\n",
-    "    * Gradients (mean and std) per layer"
+    "- Log _**debugging metrics**_ per layer during model training such as;\n",
+    "    * Activations\n",
+    "    * Gradients\n",
+    "    * Parameters (Weights and Biases)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Before you start\n",
+    "\n",
+    "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
+    "  2. Create a Neptune project that you will use for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
+    "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
    ]
   },
   {
@@ -39,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -83,8 +95,8 @@
     "\n",
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 3,\n",
-    "    \"learning_rate\": 0.001,\n",
+    "    \"batch_size\": 8,\n",
+    "    \"learning_rate\": 0.01,\n",
     "    \"epochs\": 5, \n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
@@ -100,38 +112,20 @@
    "metadata": {},
    "source": [
     "## Download or use next token prediction dataset\n",
-    "The dataset used in this example is taken from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset). In this example, you can increase the size of the dataset to test the logging capabilities of Neptune, but note that increasing the dataset size will increase the time taken for the full dataset to download. The current setup only downloads the first 10 parquet files from the Hugging Face public dataset. "
+    "The dataset used in this example is taken from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset). In this example, you can increase the size of the dataset to test the logging capabilities of Neptune, but note that increasing the dataset size will increase the time taken for the full dataset to download. The current setup only downloads the first parquet file from the Hugging Face public dataset. The validation dataset is also reduced to reduce the training loop execution time - you can increase the validation size by changing the `test_size` key-value pair in the `train_test_split()` method from HF. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
-      "        num_rows: 81926\n",
-      "    })\n",
-      "    validation: Dataset({\n",
-      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
-      "        num_rows: 9347\n",
-      "    })\n",
-      "})\n",
-      "DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
-      "        num_rows: 8412\n",
-      "    })\n",
-      "    test: Dataset({\n",
-      "        features: ['text', 'meta', '__index_level_0__', 'input_ids', 'attention_mask', 'labels'],\n",
-      "        num_rows: 935\n",
-      "    })\n",
-      "})\n"
+      "Training samples: 81926 \n",
+      "Validation samples: 935\n"
      ]
     }
    ],
@@ -144,13 +138,21 @@
     "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
     "# validation_subset = load_dataset(\"parquet\", data_files = {\"validation\": base_url + \"validation-00000-of-00001.parquet\"}, num_proc=4, split=[\"validation[:5%]\"])\n",
     "validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
-    "print(data_subset)\n",
-    "print(validation_subset)"
+    "print(f\"Training samples: {data_subset[\"train\"].num_rows} \\nValidation samples: {validation_subset[\"test\"].num_rows}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create DataLoader Objects\n",
+    "* To execute the models with PyTorch, we convert the training and validation datasets to tensors and then setup DataLoader for easier batching in our training loop.\n",
+    "* The model architecture requires the vocabulary size as an input and this we calcualte the max token from the dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -165,18 +167,12 @@
     "train_subset = data_subset[\"train\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
     "validation_subset = validation_subset[\"test\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
     "\n",
-    "train_dataloader = DataLoader(train_subset, batch_size=8, shuffle=True)\n",
-    "val_dataloader = DataLoader(validation_subset, batch_size=8, shuffle=True)\n",
+    "train_dataloader = DataLoader(train_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
+    "val_dataloader = DataLoader(validation_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
     "\n",
     "# Determine the vocab size of the dataset\n",
     "# Flatten the list of tokenized sentences into one long list of token IDs\n",
-    "all_tokens = [token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]\n",
-    "\n",
-    "# Get unique token IDs\n",
-    "# unique_tokens = set(all_tokens)\n",
-    "\n",
-    "# Vocab size is the number of unique tokens\n",
-    "vocab_size = max(all_tokens) + 1 # Add 1 since token ID's start from zero\n",
+    "vocab_size = max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
     "params[\"vocab_size\"] = vocab_size\n",
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
@@ -207,52 +203,33 @@
     "\n",
     "During model training, capturing valuable information from each layer can offer critical insights into the model's learning process, identify potential issues, and guide improvements. Monitoring certain metrics and activations helps diagnose common problems such as vanishing gradients, overfitting, or underfitting. Below are key metrics to track from each layer:\n",
     "\n",
-    "### Key Metrics to Capture from Each Layer:\n",
-    "\n",
-    "- **Activations**\n",
-    "  - **What it shows**: Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability.\n",
-    "  - **How to capture**: Use hooks to capture activations after each layer.\n",
-    "\n",
-    "- **Gradients**\n",
-    "  - **What it shows**: Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability.\n",
-    "  - **How to capture**: Use hooks to capture gradients during backpropagation.\n",
-    "\n",
-    "- **Weights and Biases**\n",
-    "  - **What it shows**: Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate.\n",
-    "  - **How to capture**: Extract directly from the model’s parameters.\n",
-    "\n",
-    "- **Layer-wise Loss**\n",
-    "  - **What it shows**: Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization.\n",
-    "  - **How to capture**: Monitor outputs from each layer and compare with the target.\n",
-    "\n",
-    "- **Learning Rate per Layer**\n",
-    "  - **What it shows**: Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate.\n",
-    "  - **How to capture**: Manually track based on optimizer settings.\n",
-    "\n",
-    "- **Layer Output Norms**\n",
-    "  - **What it shows**: The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients.\n",
-    "  - **How to capture**: Compute the L2-norm for each layer’s output.\n",
-    "\n",
-    "- **Activation Distributions**\n",
-    "  - **What it shows**: Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons.\n",
-    "  - **How to capture**: Visualize or compute statistical summaries using tools like matplotlib or seaborn.\n",
-    "\n",
-    "- **Feature Maps (for Convolutional Layers)**\n",
-    "  - **What it shows**: Offers insights into how convolutional layers detect specific patterns in the data.\n",
-    "  - **How to capture**: Visualize feature maps after convolutional layers using libraries like matplotlib.\n"
+    "### Key metrics to capture from each layer:\n",
+    "\n",
+    "| **Metric**                        | **Demonstrated in Notebook** | **What it Shows**                                                                                             | **How to Capture**                                             |\n",
+    "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
+    "| **Activations**                   | Yes                                  | Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability. | Use hooks to capture activations after each layer.            |\n",
+    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | Use hooks to capture gradients during backpropagation.        |\n",
+    "| **Weights and Biases**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
+    "| **Layer-wise Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
+    "| **Learning Rate per Layer**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
+    "| **Layer Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n",
+    "| **Activation Distributions**      | No                                   | Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons. | Visualize or compute statistical summaries using tools like matplotlib or seaborn. |\n",
+    "| **Feature Maps (for Convolutional Layers)** | No                                   | Offers insights into how convolutional layers detect specific patterns in the data. | Visualize feature maps after convolutional layers using libraries like matplotlib. |\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Define PyTorch Model Architecture and Helper functions\n",
-    "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use a mebedding layer, an LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow."
+    "### Define PyTorch Model Architecture and Helpers\n",
+    "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow. To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary or to increase the number of fully connected layers, update the mode architecture itself.\n",
+    "\n",
+    "This section also creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. You do not need to update this class as it will dynamically update according to the architecture of the model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,18 +238,14 @@
     "    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):\n",
     "        super(SimpleLLM, self).__init__()\n",
     "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, 512, num_layers = num_layers, dropout = 0.3, batch_first=True)\n",
-    "        self.fc1 = nn.Linear(512, hidden_size)\n",
-    "        self.fc2 = nn.Linear(hidden_size, vocab_size)\n",
+    "        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers = num_layers, batch_first=True)\n",
+    "        self.fc1 = nn.Linear(hidden_size, vocab_size)\n",
     "        \n",
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
     "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
     "        out = self.fc1(lstm_out) # Use the last output from the LSTM\n",
-    "        out = self.fc2(out)\n",
     "        return out\n",
-    "    \n",
-    "# model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"])\n",
     "\n",
     "# A class to manage hooks for activations and gradients\n",
     "class HookManager:\n",
@@ -342,12 +315,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Everytime a new LSTM layer is added when updating the `num_lstm_layers` parameter is changed, 12 new metrics will be logged which correspond to mean, std and norm that are calculated. "
+    "## Setup Model Training\n",
+    "### Initialize Neptune Run object and Log Hyperparameters"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -376,44 +350,65 @@
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Execute model training loop\n",
+    "In this loop, we configure the `HookManager` and register the hooks. In your training loop, you will need to use the `get_` methods to retrieve the stored values for the activations and gradients after the forward and backward passes are complete. Below, you can see a pseudo implementation:\n",
+    "\n",
+    "```python\n",
+    "# Initialize model\n",
+    "model = your_ModelClass()\n",
+    "# Register hooks\n",
+    "hm = HookManager(model)\n",
+    "hm.register_hooks()\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(3):\n",
+    "    \n",
+    "    # Forward pass, e.g. model.train()\n",
+    "    # Backward pass, e.g. loss.backward()\n",
+    "    \n",
+    "    activations = hm.get_activations()\n",
+    "    gradients = hm.get_gradients()\n",
+    "\n",
+    "    # Log values (mean, std, etc.) to Neptune\n",
+    "```\n",
+    "\n",
+    "Important: The `HookManager` class can be used in your own training script as it only accepts a model object as input."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 1 / 10241, Loss: 11.760147094726562\n",
-      "Step 2 / 10241, Loss: 11.744263648986816\n",
-      "Step 3 / 10241, Loss: 11.720343589782715\n",
-      "Step 4 / 10241, Loss: 11.64067268371582\n",
-      "Step 5 / 10241, Loss: 11.333736419677734\n",
-      "Step 5, Val_loss: 10.690058871212168\n",
-      "Activations for embedding: -0.0025795602705329657\n",
-      "Activations for lstm: -0.0010381790343672037\n",
-      "Activations for fc1: -0.07045605778694153\n",
-      "Activations for fc2: -0.679652988910675\n",
-      "Activations for : -0.679652988910675\n",
-      "Gradients for fc2: 6.027510937207733e-17\n",
-      "Gradients for fc1: 3.636175804899722e-08\n",
-      "Gradients for lstm: -3.4538189197519387e-08\n",
-      "Step 6 / 10241, Loss: 10.451458930969238\n",
-      "Step 7 / 10241, Loss: 10.109658241271973\n",
-      "Step 8 / 10241, Loss: 10.559635162353516\n",
-      "Step 9 / 10241, Loss: 9.650898933410645\n",
-      "Step 10 / 10241, Loss: 9.617280006408691\n",
-      "Step 10, Val_loss: 9.465246697776339\n",
-      "Activations for embedding: -0.003705302719026804\n",
-      "Activations for lstm: -0.008110555820167065\n",
-      "Activations for fc1: -0.14376381039619446\n",
-      "Activations for fc2: -2.851902961730957\n",
-      "Activations for : -2.851902961730957\n",
-      "Gradients for fc2: -2.2691806192661813e-16\n",
-      "Gradients for fc1: -5.234873583503941e-07\n",
-      "Gradients for lstm: 5.951231685230596e-08\n",
-      "Step 11 / 10241, Loss: 9.610464096069336\n"
+      "Step 1 / 10241, Loss: 11.760001182556152\n",
+      "Step 2 / 10241, Loss: 11.625699996948242\n",
+      "Step 3 / 10241, Loss: 9.880274772644043\n",
+      "Step 4 / 10241, Loss: 9.80041790008545\n",
+      "Step 5 / 10241, Loss: 10.201050758361816\n",
+      "Step 5, Val_loss: 9.920373557979225\n",
+      "Gradients for fc1: -3.875334975882508e-15\n",
+      "Gradients for lstm: -2.1588371055258904e-06\n",
+      "Step 6 / 10241, Loss: 9.584494590759277\n",
+      "Step 7 / 10241, Loss: 9.479751586914062\n",
+      "Step 8 / 10241, Loss: 10.132659912109375\n",
+      "Step 9 / 10241, Loss: 10.202069282531738\n",
+      "Step 10 / 10241, Loss: 11.208635330200195\n",
+      "Step 10, Val_loss: 10.601182611579569\n",
+      "Gradients for fc1: 4.1997570650103774e-15\n",
+      "Gradients for lstm: -2.5467079467489384e-06\n",
+      "Step 11 / 10241, Loss: 10.122359275817871\n",
+      "Step 12 / 10241, Loss: 10.62948226928711\n",
+      "Step 13 / 10241, Loss: 10.538959503173828\n",
+      "Step 14 / 10241, Loss: 11.84500503540039\n",
+      "Step 15 / 10241, Loss: 10.522750854492188\n"
      ]
     },
     {
@@ -423,11 +418,16 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[48], line 35\u001b[0m\n\u001b[0;32m     32\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     36\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     39\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[26], line 43\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_counter \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Do not need to log validation at every step, although we can\u001b[39;00m\n\u001b[1;32m---> 43\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvocab_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     44\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Val_loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     46\u001b[0m     \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[24], line 69\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, val_dataloader, criterion, device, vocab_size)\u001b[0m\n\u001b[0;32m     66\u001b[0m labels \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m     68\u001b[0m \u001b[38;5;66;03m# Forward pass for validation\u001b[39;00m\n\u001b[1;32m---> 69\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Shape: (batch_size, seq_len, vocab_size)\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;66;03m# Calculate the loss\u001b[39;00m\n\u001b[0;32m     72\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[24], line 12\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     10\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[0;32m     11\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm(x)  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfc1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlstm_out\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {

From 0c8497b870d0951f419a79cd2115470817f28a0e Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 12 Mar 2025 14:42:12 +0100
Subject: [PATCH 049/125] chore: cleanup commented code

---
 .../pytorch_text_model_debugging.ipynb        | 140 +-----------------
 1 file changed, 5 insertions(+), 135 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 75881c7..97c8c9c 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -177,24 +177,6 @@
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO - capture the folowings; attentions, learning rate (per layer if needed)\n",
-    "    # complete - activations, grad norms, gradients - weights+biases, loss - agg\n",
-    "# TODO - capture per layer losses\n",
-    "# TODO - increase number of layers\n",
-    "# TODO - Adding dropout to demonstrate the effects of regularization\n",
-    "# TODO - batch normalization\n",
-    "# TODO - add class for tracking activations, gradients, etc. automatically\n",
-    "# TODO - investigate packages that help with tracking layer-wise information\n",
-    "# TODO - visualize layer-wise information, activations, gradients etc. (use as example to share with product)\n",
-    "# TODO - update hooks for model, ensure tracking correct information"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -321,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,7 +312,7 @@
     "from uuid import uuid4\n",
     "\n",
     "run = Run(\n",
-    "    project = \"leo/pytorch-tutorial\",\n",
+    "    project = \"examples/pytorch-tutorial\",\n",
     "    run_id=f\"pytorch-text-{uuid4()}\"\n",
     "    )\n",
     "\n",
@@ -341,7 +323,7 @@
     "        \"config/batch_size\": params[\"batch_size\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
     "        \"config/num_lstm_layers\" : params[\"num_lstm_layers\"],\n",
-    "        # \"data/vocab_size\": params[\"vocab_size\"],\n",
+    "        \"data/vocab_size\": params[\"vocab_size\"],\n",
     "        \"data/embed_size\": params[\"embed_size\"]\n",
     "    }\n",
     ")\n",
@@ -518,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -532,121 +514,9 @@
     }
    ],
    "source": [
-    "\n",
+    "# Close run to ensure all operations are processed\n",
     "run.close()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[45], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# After training, you can analyze the activations and gradients\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# Example: Plot the activations of the LSTM layer\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m layer_name, activation \u001b[38;5;241m=\u001b[39m \u001b[43mactivations\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m  \u001b[38;5;66;03m# Assuming lstm activation\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mActivation for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m layer: \u001b[39m\u001b[38;5;124m\"\u001b[39m, activation)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Plotting histogram of activations of the LSTM layer\u001b[39;00m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 0"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# After training, you can analyze the activations and gradients\n",
-    "# Example: Plot the activations of the LSTM layer\n",
-    "layer_name, activation = activations[0]  # Assuming lstm activation\n",
-    "print(f\"Activation for {layer_name} layer: \", activation)\n",
-    "\n",
-    "# Plotting histogram of activations of the LSTM layer\n",
-    "plt.hist(activation[0].detach().numpy().flatten(), bins=100, alpha=0.7, color='b', label='LSTM Activations')\n",
-    "plt.title(f\"Activation Distribution for {layer_name}\")\n",
-    "plt.xlabel(\"Activation Value\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "# Example: Plot the gradients of the LSTM layer\n",
-    "layer_name, grad = gradients[0]  # Assuming lstm gradient\n",
-    "print(f\"Gradient for {layer_name} layer: \", grad[0].shape)\n",
-    "\n",
-    "# Plotting histogram of gradients of the LSTM layer\n",
-    "plt.hist(grad[0].numpy().flatten(), bins=100, alpha=0.7, color='r', label='LSTM Gradients')\n",
-    "plt.title(f\"Gradient Distribution for {layer_name}\")\n",
-    "plt.xlabel(\"Gradient Value\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.legend()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Activation for fc layer:  torch.Size([3, 5, 5000])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gradient for lstm layer:  torch.Size([3, 5, 256])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Example: Plot the activations of the final Linear layer\n",
-    "layer_name, activation = activations[1]  # Assuming fc activation\n",
-    "print(f\"Activation for {layer_name} layer: \", activation.shape)\n",
-    "\n",
-    "# Plotting histogram of activations of the final Linear layer\n",
-    "plt.hist(activation.detach().numpy().flatten(), bins=100, alpha=0.7, color='g', label='FC Activations')\n",
-    "plt.title(f\"Activation Distribution for {layer_name}\")\n",
-    "plt.xlabel(\"Activation Value\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "# Example: Plot the gradients of the final Linear layer\n",
-    "layer_name, grad = gradients[1]  # Assuming fc gradient\n",
-    "print(f\"Gradient for {layer_name} layer: \", grad[0].shape)\n",
-    "\n",
-    "# Plotting histogram of gradients of the final Linear layer\n",
-    "plt.hist(grad[0].numpy().flatten(), bins=100, alpha=0.7, color='y', label='FC Gradients')\n",
-    "plt.title(f\"Gradient Distribution for {layer_name}\")\n",
-    "plt.xlabel(\"Gradient Value\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.legend()\n",
-    "plt.show()\n"
-   ]
   }
  ],
  "metadata": {

From f61f734c8efbeb6b767ce6df858687fcc8acca55 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 12 Mar 2025 14:44:07 +0100
Subject: [PATCH 050/125] chore: cleanup model outputs

---
 .../pytorch_text_model_debugging.ipynb        | 64 +------------------
 1 file changed, 3 insertions(+), 61 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 97c8c9c..84435cd 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -363,57 +363,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 1 / 10241, Loss: 11.760001182556152\n",
-      "Step 2 / 10241, Loss: 11.625699996948242\n",
-      "Step 3 / 10241, Loss: 9.880274772644043\n",
-      "Step 4 / 10241, Loss: 9.80041790008545\n",
-      "Step 5 / 10241, Loss: 10.201050758361816\n",
-      "Step 5, Val_loss: 9.920373557979225\n",
-      "Gradients for fc1: -3.875334975882508e-15\n",
-      "Gradients for lstm: -2.1588371055258904e-06\n",
-      "Step 6 / 10241, Loss: 9.584494590759277\n",
-      "Step 7 / 10241, Loss: 9.479751586914062\n",
-      "Step 8 / 10241, Loss: 10.132659912109375\n",
-      "Step 9 / 10241, Loss: 10.202069282531738\n",
-      "Step 10 / 10241, Loss: 11.208635330200195\n",
-      "Step 10, Val_loss: 10.601182611579569\n",
-      "Gradients for fc1: 4.1997570650103774e-15\n",
-      "Gradients for lstm: -2.5467079467489384e-06\n",
-      "Step 11 / 10241, Loss: 10.122359275817871\n",
-      "Step 12 / 10241, Loss: 10.62948226928711\n",
-      "Step 13 / 10241, Loss: 10.538959503173828\n",
-      "Step 14 / 10241, Loss: 11.84500503540039\n",
-      "Step 15 / 10241, Loss: 10.522750854492188\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[26], line 43\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_dataloader)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss\u001b[38;5;241m.\u001b[39mitem()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_counter \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Do not need to log validation at every step, although we can\u001b[39;00m\n\u001b[1;32m---> 43\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvocab_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     44\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep_counter\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Val_loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     46\u001b[0m     \u001b[38;5;66;03m# Track activations\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[24], line 69\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(model, val_dataloader, criterion, device, vocab_size)\u001b[0m\n\u001b[0;32m     66\u001b[0m labels \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m     68\u001b[0m \u001b[38;5;66;03m# Forward pass for validation\u001b[39;00m\n\u001b[1;32m---> 69\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Shape: (batch_size, seq_len, vocab_size)\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;66;03m# Calculate the loss\u001b[39;00m\n\u001b[0;32m     72\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[24], line 12\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     10\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[0;32m     11\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm(x)  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfc1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlstm_out\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "debug_metrics = {}\n",
     "\n",
@@ -502,17 +454,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "neptune:INFO: Waiting for all operations to be processed\n",
-      "neptune:WARNING: No timeout specified. Waiting indefinitely\n",
-      "neptune:INFO: All operations were processed\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Close run to ensure all operations are processed\n",
     "run.close()"

From bcc344e9360667167f38214658da0a1b4da5bee5 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 12 Mar 2025 14:53:47 +0100
Subject: [PATCH 051/125] refactor: update quotes for dictionary keys since
 Colab returns an error

---
 .../pytorch/pytorch_text_model_debugging.ipynb              | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 84435cd..216b0f2 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +138,7 @@
     "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
     "# validation_subset = load_dataset(\"parquet\", data_files = {\"validation\": base_url + \"validation-00000-of-00001.parquet\"}, num_proc=4, split=[\"validation[:5%]\"])\n",
     "validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
-    "print(f\"Training samples: {data_subset[\"train\"].num_rows} \\nValidation samples: {validation_subset[\"test\"].num_rows}\")"
+    "print(f\"Training samples: {data_subset['train'].num_rows} \\nValidation samples: {validation_subset['test'].num_rows}\")"
    ]
   },
   {

From df89878baf6782ea2edaad3c607e7c8c07153bf4 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 14 Mar 2025 17:15:04 +0100
Subject: [PATCH 052/125] style: update intro and information about debugging
 metrics

---
 .../pytorch_text_model_debugging.ipynb        | 43 ++++++++++---------
 1 file changed, 23 insertions(+), 20 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 216b0f2..01f8a47 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -7,15 +7,18 @@
     "# Neptune + PyTorch\n",
     "\n",
     "## Logging and Visualizing debugging metrics with Neptune\n",
+    "**Global aggregated metrics**, such as overall loss and accuracy, provide a high-level view of a model's performance and help track progress toward the target task. These metrics are essential for assessing the model’s overall success and ensuring training is on the right path. However, **layer-wise metrics** offer deeper insights into how individual layers contribute to learning. By monitoring metrics like gradients and activations at each layer, we can identify specific issues (e.g., vanishing/exploding gradients) and optimize training for individual layers. This is crucial for deep networks, where different layers learn distinct types of features.\n",
     "\n",
-    "### Introduction\n",
+    "The main drawback of tracking layer-wise metrics is the **data overload** they can generate, especially with large/foundation models. Logging metrics for every layer can create significant data volume which researchers needs to log and monitor. However, with efficient tracking strategies and tools like Neptune, this challenge can be managed, allowing for detailed insights without overwhelming the training process.\n",
     "\n",
+    "### Introduction\n",
     "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. This example is designed to be used as a code recipe for you to re-use sections with your own code to edit or adapt to your own model training needs. \n",
     "\n",
     "This guide will show you how to:\n",
-    "- Initialize the Neptune Run object and log configuration parameters\n",
-    "- Log standard loss and accuracy metrics to Neptune\n",
-    "- Log _**debugging metrics**_ per layer during model training such as;\n",
+    "- Initialize the **Neptune Run** object and log configuration parameters\n",
+    "- Create a **class** to hook layer-wise metrics\n",
+    "- Log **aggregated metrics** such as loss and accuracy\n",
+    "- Log **debugging metrics** per layer during model training such as;\n",
     "    * Activations\n",
     "    * Gradients\n",
     "    * Parameters (Weights and Biases)"
@@ -51,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -73,8 +76,6 @@
     "from collections import Counter\n",
     "from datasets import load_dataset\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "from neptune_scale import Run"
    ]
   },
@@ -183,7 +184,16 @@
    "source": [
     "## Importance of Logging Debugging Metrics\n",
     "\n",
-    "During model training, capturing valuable information from each layer can offer critical insights into the model's learning process, identify potential issues, and guide improvements. Monitoring certain metrics and activations helps diagnose common problems such as vanishing gradients, overfitting, or underfitting. Below are key metrics to track from each layer:\n",
+    "Tracking layer-wise metrics during training of foundation models is an important aspect of understanding and improving the performance of large-scale neural networks. Tracking layer-wise metrics is imporant to;\n",
+    "1) Understand the model behaviour at different depths\n",
+    "2) Diagnose training issues (vanishing/exploding gradients)\n",
+    "3) Model interpretability and debugging\n",
+    "4) Layer-specific regularization\n",
+    "5) Transfer learning insights\n",
+    "\n",
+    "However, one drawback is the overwhelming amount of data generated that needs to be logged, tracked and analyzed. These models can have hundreds to thousands of layers making the data difficult to interpret. However, with Neptune this is not a problem. \n",
+    "\n",
+    "In this example, we will show you that it is possible to log and visualize layer-wise metrics such as activations, gradients as well as global metrics.\n",
     "\n",
     "### Key metrics to capture from each layer:\n",
     "\n",
@@ -192,9 +202,9 @@
     "| **Activations**                   | Yes                                  | Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability. | Use hooks to capture activations after each layer.            |\n",
     "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | Use hooks to capture gradients during backpropagation.        |\n",
     "| **Weights and Biases**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
-    "| **Layer-wise Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
-    "| **Learning Rate per Layer**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
-    "| **Layer Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n",
+    "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
+    "| **Learning Rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
+    "| **Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n",
     "| **Activation Distributions**      | No                                   | Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons. | Visualize or compute statistical summaries using tools like matplotlib or seaborn. |\n",
     "| **Feature Maps (for Convolutional Layers)** | No                                   | Offers insights into how convolutional layers detect specific patterns in the data. | Visualize feature maps after convolutional layers using libraries like matplotlib. |\n"
    ]
@@ -447,15 +457,8 @@
     "    print(f\"Epoch {epoch + 1}, Loss: {total_loss / len(train_dataloader)}\")\n",
     "\n",
     "# test_loss = evaluate_model(model, test_input, test_target, params[\"vocab_size\"])\n",
-    "# print(f'Test Loss: {test_loss:.4f}')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "# print(f'Test Loss: {test_loss:.4f}')\n",
+    "\n",
     "# Close run to ensure all operations are processed\n",
     "run.close()"
    ]

From 23bd58a80d37a48316e003801cf2fae42f2602ff Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 17 Mar 2025 16:26:44 +0100
Subject: [PATCH 053/125] refactor: update to be able to run model on GPUs

---
 .../pytorch_image_classification.ipynb        | 115 +++++++++++++++++-
 1 file changed, 110 insertions(+), 5 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
index 0fd0e1c..01ca6d4 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
@@ -117,7 +117,105 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 404: Not Found\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 9912422/9912422 [00:11<00:00, 883546.49it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 404: Not Found\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 28881/28881 [00:00<00:00, 29159.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 404: Not Found\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1648877/1648877 [00:01<00:00, 1201389.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 404: Not Found\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4542/4542 [00:00<00:00, 2459084.65it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "# Transform to normalize the data and convert it to tensor\n",
@@ -139,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -213,6 +311,8 @@
     "# Instantiate the model, loss function, and optimizer\n",
     "#model = SimpleCNN()\n",
     "model = SimpleNN()\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "model.to(device)\n",
     "criterion = nn.CrossEntropyLoss()  # Loss function\n",
     "\n",
     "# Select an optimizer\n",
@@ -228,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,6 +340,9 @@
     "    epoch_loss = 0\n",
     "    with torch.no_grad():  # Disable gradient tracking during evaluation\n",
     "        for data, target in data_loader:\n",
+    "            \n",
+    "            data, target = data.to(device), target.to(device)\n",
+    "            \n",
     "            # Forward pass (with gradient tracking if specified)\n",
     "            output = model(data)\n",
     "            loss = criterion(output, target)  # Correct loss computation\n",
@@ -267,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -348,6 +451,8 @@
     "        step_counter += 1\n",
     "        optimizer.zero_grad()\n",
     "        \n",
+    "        data, target = data.to(device), target.to(device)\n",
+    "\n",
     "        # Forward pass\n",
     "        output = model(data)\n",
     "        \n",

From 0dbd34134cc5736bf0261a8eb2134fb64bf5ea1b Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 17 Mar 2025 17:51:41 +0100
Subject: [PATCH 054/125] style: update ending of notebook with follow along

---
 .../pytorch_text_model_debugging.ipynb        | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 01f8a47..2d22ee5 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -462,6 +462,29 @@
     "# Close run to ensure all operations are processed\n",
     "run.close()"
    ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What next?\n",
+    "\n",
+    "Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evovle with each training step. Filter through each metric quickly to find the metrics you're most interested in. Use dashboards to create custom analysis of the layer-wise metrics like gradients and activations. Create dyanmic charts with advanced regex searching to add new layers to charts as you update and modify the model architecture.\n",
+    "\n",
+    "\n",
+    "### Advanced Regex searching\n",
+    "Navigate through hundreds of metrics super fast.\n",
+    "\n",
+    "TODO - can we add gifs to show some cool UI things?\n",
+    "\n",
+    "### Create dynamic charts\n",
+    "![image.png](attachment:image.png)"
+   ]
   }
  ],
  "metadata": {

From 8f98b334f89c8f71699aabe13aefd3477e2b2104 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 18 Mar 2025 10:07:13 +0100
Subject: [PATCH 055/125] style: add colab link to intro

---
 .../pytorch/pytorch_text_model_debugging.ipynb                | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 2d22ee5..d749587 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -6,6 +6,10 @@
    "source": [
     "# Neptune + PyTorch\n",
     "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lr%2Fpytorch_example/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb\"> \n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
+    "</a>\n",
+    "\n",
     "## Logging and Visualizing debugging metrics with Neptune\n",
     "**Global aggregated metrics**, such as overall loss and accuracy, provide a high-level view of a model's performance and help track progress toward the target task. These metrics are essential for assessing the model’s overall success and ensuring training is on the right path. However, **layer-wise metrics** offer deeper insights into how individual layers contribute to learning. By monitoring metrics like gradients and activations at each layer, we can identify specific issues (e.g., vanishing/exploding gradients) and optimize training for individual layers. This is crucial for deep networks, where different layers learn distinct types of features.\n",
     "\n",

From ce66396f1f18b8cba4868b5bb3a791e7b6827afe Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 18 Mar 2025 10:19:57 +0100
Subject: [PATCH 056/125] style: update colab link and ending section

---
 .../pytorch/pytorch_text_model_debugging.ipynb               | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index d749587..239a59e 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -478,7 +478,10 @@
    "source": [
     "## What next?\n",
     "\n",
-    "Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evovle with each training step. Filter through each metric quickly to find the metrics you're most interested in. Use dashboards to create custom analysis of the layer-wise metrics like gradients and activations. Create dyanmic charts with advanced regex searching to add new layers to charts as you update and modify the model architecture.\n",
+    "1) Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evolve with each training step and epoch. \n",
+    "2) [Filter through each metric](https://docs-beta.neptune.ai/charts#filtering-charts) quickly to find the metrics you're most interested in. \n",
+    "3) Use [dashboards](https://docs-beta.neptune.ai/custom_dashboard) to create custom analysis of the layer-wise metrics like gradients and activations. \n",
+    "4) Create [dynamic charts](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) with advanced regex searching to add new layers to charts as you update and modify the model architecture.\n",
     "\n",
     "\n",
     "### Advanced Regex searching\n",

From 5eda85665d66f18ef048c3d19b9a6b59afbfad59 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 18 Mar 2025 10:34:09 +0100
Subject: [PATCH 057/125] style: update intro to notebook

---
 .../pytorch_text_model_debugging.ipynb        | 85 +++++--------------
 1 file changed, 23 insertions(+), 62 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 239a59e..6e10b7f 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -20,12 +20,27 @@
     "\n",
     "This guide will show you how to:\n",
     "- Initialize the **Neptune Run** object and log configuration parameters\n",
-    "- Create a **class** to hook layer-wise metrics\n",
+    "- Create a **reuseable class** to hook layer-wise metrics\n",
     "- Log **aggregated metrics** such as loss and accuracy\n",
     "- Log **debugging metrics** per layer during model training such as;\n",
     "    * Activations\n",
     "    * Gradients\n",
-    "    * Parameters (Weights and Biases)"
+    "    * Parameters (Weights and Biases)\n",
+    "\n",
+    "There are also several other metrics that we can capture to understanding our model training in more depth, but we will cover those in another tutorial.\n",
+    "\n",
+    "### Key metrics to capture from each layer:\n",
+    "\n",
+    "| **Metric**                        | **Demonstrated in Notebook** | **What it Shows**                                                                                             | **How to Capture**                                             |\n",
+    "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
+    "| **Activations**                   | Yes                                  | Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability. | Use hooks to capture activations after each layer.            |\n",
+    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | Use hooks to capture gradients during backpropagation.        |\n",
+    "| **Weights and Biases**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
+    "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
+    "| **Learning Rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
+    "| **Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n",
+    "| **Activation Distributions**      | No                                   | Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons. | Visualize or compute statistical summaries using tools like matplotlib or seaborn. |\n",
+    "| **Feature Maps (for Convolutional Layers)** | No                                   | Offers insights into how convolutional layers detect specific patterns in the data. | Visualize feature maps after convolutional layers using libraries like matplotlib. |\n"
    ]
   },
   {
@@ -58,18 +73,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -152,7 +158,7 @@
    "source": [
     "## Create DataLoader Objects\n",
     "* To execute the models with PyTorch, we convert the training and validation datasets to tensors and then setup DataLoader for easier batching in our training loop.\n",
-    "* The model architecture requires the vocabulary size as an input and this we calcualte the max token from the dataset."
+    "* The model architecture requires the vocabulary size as an input and this we calculate the max token from the dataset."
    ]
   },
   {
@@ -182,37 +188,6 @@
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importance of Logging Debugging Metrics\n",
-    "\n",
-    "Tracking layer-wise metrics during training of foundation models is an important aspect of understanding and improving the performance of large-scale neural networks. Tracking layer-wise metrics is imporant to;\n",
-    "1) Understand the model behaviour at different depths\n",
-    "2) Diagnose training issues (vanishing/exploding gradients)\n",
-    "3) Model interpretability and debugging\n",
-    "4) Layer-specific regularization\n",
-    "5) Transfer learning insights\n",
-    "\n",
-    "However, one drawback is the overwhelming amount of data generated that needs to be logged, tracked and analyzed. These models can have hundreds to thousands of layers making the data difficult to interpret. However, with Neptune this is not a problem. \n",
-    "\n",
-    "In this example, we will show you that it is possible to log and visualize layer-wise metrics such as activations, gradients as well as global metrics.\n",
-    "\n",
-    "### Key metrics to capture from each layer:\n",
-    "\n",
-    "| **Metric**                        | **Demonstrated in Notebook** | **What it Shows**                                                                                             | **How to Capture**                                             |\n",
-    "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
-    "| **Activations**                   | Yes                                  | Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability. | Use hooks to capture activations after each layer.            |\n",
-    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | Use hooks to capture gradients during backpropagation.        |\n",
-    "| **Weights and Biases**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
-    "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
-    "| **Learning Rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
-    "| **Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n",
-    "| **Activation Distributions**      | No                                   | Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons. | Visualize or compute statistical summaries using tools like matplotlib or seaborn. |\n",
-    "| **Feature Maps (for Convolutional Layers)** | No                                   | Offers insights into how convolutional layers detect specific patterns in the data. | Visualize feature maps after convolutional layers using libraries like matplotlib. |\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -220,7 +195,7 @@
     "### Define PyTorch Model Architecture and Helpers\n",
     "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow. To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary or to increase the number of fully connected layers, update the mode architecture itself.\n",
     "\n",
-    "This section also creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. You do not need to update this class as it will dynamically update according to the architecture of the model."
+    "This section also creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. You do not need to update this class as it will dynamically update according to the architecture of the model. This class is also resuable and can be copied into your own code."
    ]
   },
   {
@@ -468,29 +443,15 @@
    ]
   },
   {
-   "attachments": {
-    "image.png": {
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"
-    }
-   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## What next?\n",
+    "## What's next?\n",
     "\n",
     "1) Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evolve with each training step and epoch. \n",
     "2) [Filter through each metric](https://docs-beta.neptune.ai/charts#filtering-charts) quickly to find the metrics you're most interested in. \n",
     "3) Use [dashboards](https://docs-beta.neptune.ai/custom_dashboard) to create custom analysis of the layer-wise metrics like gradients and activations. \n",
-    "4) Create [dynamic charts](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) with advanced regex searching to add new layers to charts as you update and modify the model architecture.\n",
-    "\n",
-    "\n",
-    "### Advanced Regex searching\n",
-    "Navigate through hundreds of metrics super fast.\n",
-    "\n",
-    "TODO - can we add gifs to show some cool UI things?\n",
-    "\n",
-    "### Create dynamic charts\n",
-    "![image.png](attachment:image.png)"
+    "4) Create [dynamic charts](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) with advanced regex searching to add new layers to charts as you update and modify the model architecture."
    ]
   }
  ],

From ffd0b0a933e7d667bfc0a089cbe4d766aacc68c3 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 18 Mar 2025 10:36:32 +0100
Subject: [PATCH 058/125] style: update markdown display to work with colab

---
 .../pytorch/pytorch_text_model_debugging.ipynb            | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 6e10b7f..17445f6 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -448,10 +448,10 @@
    "source": [
     "## What's next?\n",
     "\n",
-    "1) Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evolve with each training step and epoch. \n",
-    "2) [Filter through each metric](https://docs-beta.neptune.ai/charts#filtering-charts) quickly to find the metrics you're most interested in. \n",
-    "3) Use [dashboards](https://docs-beta.neptune.ai/custom_dashboard) to create custom analysis of the layer-wise metrics like gradients and activations. \n",
-    "4) Create [dynamic charts](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) with advanced regex searching to add new layers to charts as you update and modify the model architecture."
+    "1. Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evolve with each training step and epoch. \n",
+    "2. [Filter through each metric](https://docs-beta.neptune.ai/charts#filtering-charts) quickly to find the metrics you're most interested in. \n",
+    "3. Use [dashboards](https://docs-beta.neptune.ai/custom_dashboard) to create custom analysis of the layer-wise metrics like gradients and activations. \n",
+    "4. Create [dynamic charts](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) with advanced regex searching to add new layers to charts as you update and modify the model architecture."
    ]
   }
  ],

From 275f0241df13e424ff5b401b25e6b29fc206db80 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 18 Mar 2025 13:45:18 +0100
Subject: [PATCH 059/125] style: update HookManager section

---
 .../pytorch_text_model_debugging.ipynb        | 109 ++++++++++--------
 1 file changed, 63 insertions(+), 46 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 17445f6..904594f 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -193,14 +193,12 @@
    "metadata": {},
    "source": [
     "### Define PyTorch Model Architecture and Helpers\n",
-    "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow. To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary or to increase the number of fully connected layers, update the mode architecture itself.\n",
-    "\n",
-    "This section also creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. You do not need to update this class as it will dynamically update according to the architecture of the model. This class is also resuable and can be copied into your own code."
+    "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow. To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary or to increase the number of fully connected layers, update the mode architecture itself."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,9 +216,68 @@
     "        out = self.fc1(lstm_out) # Use the last output from the LSTM\n",
     "        return out\n",
     "\n",
+    "# Function to evaluate the model after each epoch/step\n",
+    "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    total_loss = 0\n",
+    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
+    "        for batch in val_dataloader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "\n",
+    "            # Forward pass for validation\n",
+    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
+    "            \n",
+    "            # Calculate the loss\n",
+    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "    avg_val_loss = total_loss / len(val_dataloader)\n",
+    "    return avg_val_loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Tracking Class (HooKManager)\n",
+    "\n",
+    "This section creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. This class accepts a PyTorch model object as an input. This class will automatically capture the gradients and activations in each layer of the model. Below, you can see a pseudo implementation:\n",
+    "\n",
+    "```python\n",
+    "# Initialize model\n",
+    "model = your_ModelClass()\n",
+    "# Register hooks\n",
+    "hm = HookManager(model)\n",
+    "hm.register_hooks()\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(3):\n",
+    "    \n",
+    "    # Forward pass, e.g. model.train()\n",
+    "    # Backward pass, e.g. loss.backward()\n",
+    "    \n",
+    "    activations = hm.get_activations()\n",
+    "    gradients = hm.get_gradients()\n",
+    "\n",
+    "    # Log values (mean, std, etc.) to Neptune\n",
+    "```\n",
+    "\n",
+    "_Important_: The `HookManager` class can be used in your own training script as it only accepts a model object as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# A class to manage hooks for activations and gradients\n",
     "class HookManager:\n",
     "    def __init__(self, model):\n",
+    "        if not isinstance(model, nn.Module):\n",
+    "            raise TypeError(\"The model must be a PyTorch model\")\n",
+    "        \n",
     "        self.model = model\n",
     "        self.hooks = []\n",
     "        self.activations = {}\n",
@@ -260,26 +317,7 @@
     "\n",
     "    # Function to get gradients\n",
     "    def get_gradients(self):\n",
-    "        return self.gradients\n",
-    "\n",
-    "# Function to evaluate the model after each epoch/step\n",
-    "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
-    "    model.eval()  # Set the model to evaluation mode\n",
-    "    total_loss = 0\n",
-    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
-    "        for batch in val_dataloader:\n",
-    "            input_ids = batch['input_ids'].to(device)\n",
-    "            labels = batch['labels'].to(device)\n",
-    "\n",
-    "            # Forward pass for validation\n",
-    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
-    "            \n",
-    "            # Calculate the loss\n",
-    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "            total_loss += loss.item()\n",
-    "\n",
-    "    avg_val_loss = total_loss / len(val_dataloader)\n",
-    "    return avg_val_loss"
+    "        return self.gradients"
    ]
   },
   {
@@ -326,28 +364,7 @@
    "metadata": {},
    "source": [
     "### Execute model training loop\n",
-    "In this loop, we configure the `HookManager` and register the hooks. In your training loop, you will need to use the `get_` methods to retrieve the stored values for the activations and gradients after the forward and backward passes are complete. Below, you can see a pseudo implementation:\n",
-    "\n",
-    "```python\n",
-    "# Initialize model\n",
-    "model = your_ModelClass()\n",
-    "# Register hooks\n",
-    "hm = HookManager(model)\n",
-    "hm.register_hooks()\n",
-    "\n",
-    "# Training loop\n",
-    "for epoch in range(3):\n",
-    "    \n",
-    "    # Forward pass, e.g. model.train()\n",
-    "    # Backward pass, e.g. loss.backward()\n",
-    "    \n",
-    "    activations = hm.get_activations()\n",
-    "    gradients = hm.get_gradients()\n",
-    "\n",
-    "    # Log values (mean, std, etc.) to Neptune\n",
-    "```\n",
-    "\n",
-    "Important: The `HookManager` class can be used in your own training script as it only accepts a model object as input."
+    "In this loop, we configure the `HookManager` and register the hooks. In your training loop, you will need to use the `get_` methods to retrieve the stored values for the activations and gradients after the forward and backward passes are complete."
    ]
   },
   {

From 295c9b2a53c0f2a27a6288818245dc582a0049b1 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 19 Mar 2025 09:57:49 +0100
Subject: [PATCH 060/125] style: update sections headings

---
 .../pytorch/pytorch_text_model_debugging.ipynb   | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 904594f..6e2755c 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -10,7 +10,7 @@
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
     "</a>\n",
     "\n",
-    "## Logging and Visualizing debugging metrics with Neptune\n",
+    "## Logging and visualizing debugging metrics with Neptune\n",
     "**Global aggregated metrics**, such as overall loss and accuracy, provide a high-level view of a model's performance and help track progress toward the target task. These metrics are essential for assessing the model’s overall success and ensuring training is on the right path. However, **layer-wise metrics** offer deeper insights into how individual layers contribute to learning. By monitoring metrics like gradients and activations at each layer, we can identify specific issues (e.g., vanishing/exploding gradients) and optimize training for individual layers. This is crucial for deep networks, where different layers learn distinct types of features.\n",
     "\n",
     "The main drawback of tracking layer-wise metrics is the **data overload** they can generate, especially with large/foundation models. Logging metrics for every layer can create significant data volume which researchers needs to log and monitor. However, with efficient tracking strategies and tools like Neptune, this challenge can be managed, allowing for detailed insights without overwhelming the training process.\n",
@@ -58,7 +58,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Install Dependencies and Import Libraries"
+    "### Install dependencies and import libraries"
    ]
   },
   {
@@ -93,7 +93,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialize Parameters"
+    "### Initialize parameters"
    ]
   },
   {
@@ -156,7 +156,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create DataLoader Objects\n",
+    "## Create `DataLoader` objects\n",
     "* To execute the models with PyTorch, we convert the training and validation datasets to tensors and then setup DataLoader for easier batching in our training loop.\n",
     "* The model architecture requires the vocabulary size as an input and this we calculate the max token from the dataset."
    ]
@@ -192,7 +192,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Define PyTorch Model Architecture and Helpers\n",
+    "### Define PyTorch model architecture and helpers\n",
     "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow. To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary or to increase the number of fully connected layers, update the mode architecture itself."
    ]
   },
@@ -240,7 +240,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create Tracking Class (HooKManager)\n",
+    "### Create tracking class\n",
     "\n",
     "This section creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. This class accepts a PyTorch model object as an input. This class will automatically capture the gradients and activations in each layer of the model. Below, you can see a pseudo implementation:\n",
     "\n",
@@ -324,8 +324,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup Model Training\n",
-    "### Initialize Neptune Run object and Log Hyperparameters"
+    "## Setup model training\n",
+    "### Initialize Neptune run object and log hyperparameters"
    ]
   },
   {

From 585184245a4369201f3dfc44ae05429a188ca7de Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 19 Mar 2025 10:12:22 +0100
Subject: [PATCH 061/125] style: condense intro

---
 .../pytorch_text_model_debugging.ipynb        | 30 +++++++------------
 1 file changed, 10 insertions(+), 20 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 6e2755c..443e417 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -11,36 +11,26 @@
     "</a>\n",
     "\n",
     "## Logging and visualizing debugging metrics with Neptune\n",
-    "**Global aggregated metrics**, such as overall loss and accuracy, provide a high-level view of a model's performance and help track progress toward the target task. These metrics are essential for assessing the model’s overall success and ensuring training is on the right path. However, **layer-wise metrics** offer deeper insights into how individual layers contribute to learning. By monitoring metrics like gradients and activations at each layer, we can identify specific issues (e.g., vanishing/exploding gradients) and optimize training for individual layers. This is crucial for deep networks, where different layers learn distinct types of features.\n",
-    "\n",
-    "The main drawback of tracking layer-wise metrics is the **data overload** they can generate, especially with large/foundation models. Logging metrics for every layer can create significant data volume which researchers needs to log and monitor. However, with efficient tracking strategies and tools like Neptune, this challenge can be managed, allowing for detailed insights without overwhelming the training process.\n",
+    "Global metrics (e.g., loss, accuracy) provide a high-level performance snapshot and ensure training is on course. However, for large or foundation models, monitoring layer-wise metrics—such as gradients and activations—delivers critical insights into how each layer learns. This level of detail helps identify issues (e.g., vanishing/exploding gradients) and fine-tune individual layers for better overall performance.\n",
+    "The **main challenge** is the sheer volume of data generated by layer-wise logging. Fortunately, Neptune is built for hyperscale tracking, enabling you to efficiently capture, organize, and analyze metrics from every layer—no matter how large your model—without disrupting the training process.\n",
     "\n",
     "### Introduction\n",
-    "See how Neptune Scale can be used for pre-training models like foundation models by tracking hundreds of metrics. This example is designed to be used as a code recipe for you to re-use sections with your own code to edit or adapt to your own model training needs. \n",
+    "This example is designed to be used as a code recipe for you to re-use sections with your own code to edit or adapt to your own model training needs. \n",
     "\n",
     "This guide will show you how to:\n",
     "- Initialize the **Neptune Run** object and log configuration parameters\n",
-    "- Create a **reuseable class** to hook layer-wise metrics\n",
+    "- Create a **reuseable class** to hook layer-wise metrics (`HookManager`)\n",
     "- Log **aggregated metrics** such as loss and accuracy\n",
-    "- Log **debugging metrics** per layer during model training such as;\n",
-    "    * Activations\n",
-    "    * Gradients\n",
-    "    * Parameters (Weights and Biases)\n",
-    "\n",
-    "There are also several other metrics that we can capture to understanding our model training in more depth, but we will cover those in another tutorial.\n",
-    "\n",
-    "### Key metrics to capture from each layer:\n",
+    "- Log **layer-wise metrics** to debug model traing such as;\n",
     "\n",
-    "| **Metric**                        | **Demonstrated in Notebook** | **What it Shows**                                                                                             | **How to Capture**                                             |\n",
+    "| **Metric**                        | **Demonstrated** | **What it Shows**                                                                                             | **How to Capture**                                             |\n",
     "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
-    "| **Activations**                   | Yes                                  | Provides insight into how the model is processing data. Dead or exploding activations can indicate issues with training stability. | Use hooks to capture activations after each layer.            |\n",
-    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | Use hooks to capture gradients during backpropagation.        |\n",
-    "| **Weights and Biases**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
+    "| **Activations**                   | Yes                                  | Dead or exploding activations can indicate issues with training stability. | `HookManager`            |\n",
+    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | `HookManager`        |\n",
+    "| **Parameters**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
     "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
     "| **Learning Rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
-    "| **Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n",
-    "| **Activation Distributions**      | No                                   | Helps diagnose saturation issues, especially with ReLU activations that may lead to dead neurons. | Visualize or compute statistical summaries using tools like matplotlib or seaborn. |\n",
-    "| **Feature Maps (for Convolutional Layers)** | No                                   | Offers insights into how convolutional layers detect specific patterns in the data. | Visualize feature maps after convolutional layers using libraries like matplotlib. |\n"
+    "| **Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n"
    ]
   },
   {

From ea30d40c4ba400049fb5601f58a31b03b0c06f91 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 19 Mar 2025 18:27:31 +0100
Subject: [PATCH 062/125] style: add links for active run and update ending
 steps

---
 .../pytorch_text_model_debugging.ipynb        | 54 +++++++++++++------
 1 file changed, 38 insertions(+), 16 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 443e417..3b6fcff 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,8 @@
     "from collections import Counter\n",
     "from datasets import load_dataset\n",
     "\n",
-    "from neptune_scale import Run"
+    "from neptune_scale import Run\n",
+    "import os"
    ]
   },
   {
@@ -88,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -153,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -188,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,17 +321,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "View active run: https://scale.neptune.ai/leo/pytorch-tutorial/-/run/?customId=pytorch-text-233cfd51-ae85-4e8f-af8d-b5ce553b7aec\n"
+     ]
+    }
+   ],
    "source": [
     "# Define Neptune parameters\n",
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
     "\n",
+    "custom_run_id = f\"pytorch-text-{uuid4()}\" # Create your own custom run_id\n",
+    "\n",
     "run = Run(\n",
-    "    project = \"examples/pytorch-tutorial\",\n",
-    "    run_id=f\"pytorch-text-{uuid4()}\"\n",
+    "    project = \"leo/pytorch-tutorial\",\n",
+    "    run_id=custom_run_id\n",
     "    )\n",
     "\n",
     "run.log_configs(\n",
@@ -346,7 +357,9 @@
     ")\n",
     "\n",
     "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
-    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])"
+    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])\n",
+    "\n",
+    "print(f\"View active run: https://scale.neptune.ai/{os.getenv('NEPTUNE_PROJECT')}/-/run/?customId={custom_run_id}\")"
    ]
   },
   {
@@ -363,6 +376,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Create links to access active training run. Note - this is not required for the example to run, but for ease of illustration of the tutorial.\n",
+    "print(f\"View training charts: https://scale.neptune.ai/{os.getenv('NEPTUNE_PROJECT')}/-/run/?customId={custom_run_id}&detailsTab=charts\")\n",
+    "\n",
+    "# Training setup\n",
     "debug_metrics = {}\n",
     "\n",
     "# Initialize model and optimizer\n",
@@ -454,11 +471,16 @@
    "metadata": {},
    "source": [
     "## What's next?\n",
+    "While the model is training, you can start using our _super-snappy_ web UI to browse your metrics and create custom analyses and visualizations.\n",
+    "\n",
+    "### What to do\n",
+    "1. Navigate to the charts tab of the active run to visualize the large number of metrics being logged in near real time.\n",
+    "2. Filter the metrics using our [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, use the following query in the search bar; `.*gradient+.*fc\\d`. This query will filter all metrics for the gradients of the fully connected layers. The more FC layers, the more charts will appear. \n",
+    "3. Export this filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard), using the _**Duplicate to**_ button. The saved dashboard will now only display these metrics during training.\n",
+    "4. Using our [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) feature, you can update the chart widget to display all fully connected layers gradients in one chart. Use the same query as above in the chart widget.\n",
+    "5. Create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics and more.\n",
     "\n",
-    "1. Navigate to the [Neptune Web App](https://scale.neptune.ai/) and visualize your runs in near real-time. See how each of the metrics evolve with each training step and epoch. \n",
-    "2. [Filter through each metric](https://docs-beta.neptune.ai/charts#filtering-charts) quickly to find the metrics you're most interested in. \n",
-    "3. Use [dashboards](https://docs-beta.neptune.ai/custom_dashboard) to create custom analysis of the layer-wise metrics like gradients and activations. \n",
-    "4. Create [dynamic charts](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) with advanced regex searching to add new layers to charts as you update and modify the model architecture."
+    "You can see a generic example of the end result [here](https://scale.neptune.ai/o/examples/org/LLM-Pretraining/reports/9e6a2cad-77e7-42df-9d64-28f07d37e908).\n"
    ]
   }
  ],

From 471beb7224b49e3a27cb5422496437cc1467154f Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 19 Mar 2025 18:42:43 +0100
Subject: [PATCH 063/125] refactor: log global and debugging metrics during
 training at each step and only validation loss logged every 50 steps

---
 .../pytorch_text_model_debugging.ipynb        | 76 ++++++++++---------
 1 file changed, 42 insertions(+), 34 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 3b6fcff..9289af0 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,14 +321,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "View active run: https://scale.neptune.ai/leo/pytorch-tutorial/-/run/?customId=pytorch-text-233cfd51-ae85-4e8f-af8d-b5ce553b7aec\n"
+      "View active run: https://scale.neptune.ai/leo/pytorch-tutorial/-/run/?customId=pytorch-text-d328336d-8fe1-4271-bf0d-53e4e5ea36a2\n"
      ]
     }
    ],
@@ -421,33 +421,31 @@
     "        total_loss += loss.item()\n",
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
     "\n",
-    "        if step_counter % 5 == 0: # Do not need to log validation at every step, although we can\n",
+    "        # Track activations\n",
+    "        activations = hook_manager.get_activations()\n",
+    "        for layer, activation in activations.items():\n",
+    "            if layer is not None:\n",
+    "                debug_metrics[f\"debug/activation/{layer}_mean\"] = activation[0].mean().item()\n",
+    "                debug_metrics[f\"debug/activation/{layer}_std\"] = activation[0].std().item()\n",
+    "\n",
+    "        # Track gradients with hooks\n",
+    "        gradients = hook_manager.get_gradients()\n",
+    "        for layer, gradient in gradients.items():\n",
+    "            debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
+    "            print(f\"Gradients for {layer}: {gradient.mean().item()}\") # You can replace to use mean(), sum(), max() or min()\n",
+    "            # simplified_gradient = gradient.mean(dim=(0, 1))\n",
+    "            # print(f\"Summed Gradient for {layer}: {simplified_gradient}\")\n",
+    "\n",
+    "        # Track gradients per layer at each epoch\n",
+    "        for layer, param in model.named_parameters():\n",
+    "            if param is not None:\n",
+    "                debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
+    "                debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
+    "                debug_metrics[f\"debug/parameters/{layer}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
+    "\n",
+    "        if step_counter % 50 == 0: # Log validation loss at every 30 steps\n",
     "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",
-    "            print(f\"Step {step_counter}, Val_loss: {val_loss}\")\n",
-    "\n",
-    "            # Track activations\n",
-    "            activations = hook_manager.get_activations()\n",
-    "            for layer, activation in activations.items():\n",
-    "                if layer is not None:\n",
-    "                    debug_metrics[f\"debug/activation/{layer}_mean\"] = activation[0].mean().item()\n",
-    "                    debug_metrics[f\"debug/activation/{layer}_std\"] = activation[0].std().item()\n",
-    "\n",
-    "            # Track gradients with hooks\n",
-    "            gradients = hook_manager.get_gradients()\n",
-    "            for layer, gradient in gradients.items():\n",
-    "               debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
-    "               print(f\"Gradients for {layer}: {gradient.mean().item()}\") # You can replace to use mean(), sum(), max() or min()\n",
-    "               # simplified_gradient = gradient.mean(dim=(0, 1))\n",
-    "               # print(f\"Summed Gradient for {layer}: {simplified_gradient}\")\n",
-    "\n",
-    "            # Track gradients per layer at each epoch\n",
-    "            for layer, param in model.named_parameters():\n",
-    "                if param is not None:\n",
-    "                    debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
-    "                    debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
-    "                    debug_metrics[f\"debug/parameters/{layer}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
-    "\n",
-    "            # Output loss for this epoch\n",
+    "\n",
     "            run.log_metrics(\n",
     "                data = {\n",
     "                    \"metrics/train/loss\": loss.item(),\n",
@@ -457,6 +455,16 @@
     "                },\n",
     "                step = step_counter\n",
     "            )\n",
+    "        else: # Log training loss and debugging metrics for each step\n",
+    "            run.log_metrics(\n",
+    "                data = {\n",
+    "                    \"metrics/train/loss\": loss.item(),\n",
+    "                    \"epoch/value\": epoch,\n",
+    "                    **debug_metrics\n",
+    "                },\n",
+    "                step = step_counter\n",
+    "            )\n",
+    "        \n",
     "    print(f\"Epoch {epoch + 1}, Loss: {total_loss / len(train_dataloader)}\")\n",
     "\n",
     "# test_loss = evaluate_model(model, test_input, test_target, params[\"vocab_size\"])\n",

From b2732cf9aa3dff0dabb7b53d581864ba5be7153b Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 09:42:37 +0100
Subject: [PATCH 064/125] style: update urls to use experiment_name

---
 .../pytorch_text_model_debugging.ipynb        | 38 ++++++++++++-------
 1 file changed, 25 insertions(+), 13 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 9289af0..106c3db 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -63,9 +63,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -89,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,14 +330,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "View active run: https://scale.neptune.ai/leo/pytorch-tutorial/-/run/?customId=pytorch-text-d328336d-8fe1-4271-bf0d-53e4e5ea36a2\n"
+      "View current experiment: https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?viewId=standard-view&runIdentificationKey=pytorch-text&type=experiment\n"
      ]
     }
    ],
@@ -337,11 +346,14 @@
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
     "\n",
+    "project = \"leo/pytorch-tutorial\"\n",
     "custom_run_id = f\"pytorch-text-{uuid4()}\" # Create your own custom run_id\n",
+    "experiment_name = \"pytorch-text\" # Create a run that is the head of an experiment. This will also be used for forking.\n",
     "\n",
     "run = Run(\n",
-    "    project = \"leo/pytorch-tutorial\",\n",
-    "    run_id=custom_run_id\n",
+    "    project = project,\n",
+    "    run_id=custom_run_id,\n",
+    "    experiment_name = experiment_name,\n",
     "    )\n",
     "\n",
     "run.log_configs(\n",
@@ -359,7 +371,7 @@
     "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])\n",
     "\n",
-    "print(f\"View active run: https://scale.neptune.ai/{os.getenv('NEPTUNE_PROJECT')}/-/run/?customId={custom_run_id}\")"
+    "print(f\"View current experiment: https://scale.neptune.ai/{project}/runs/details?viewId=standard-view&runIdentificationKey={experiment_name}&type=experiment\")"
    ]
   },
   {
@@ -377,7 +389,7 @@
    "outputs": [],
    "source": [
     "# Create links to access active training run. Note - this is not required for the example to run, but for ease of illustration of the tutorial.\n",
-    "print(f\"View training charts: https://scale.neptune.ai/{os.getenv('NEPTUNE_PROJECT')}/-/run/?customId={custom_run_id}&detailsTab=charts\")\n",
+    "print(f\"View training charts: https://scale.neptune.ai/{project}/-/run/?customId={custom_run_id}&detailsTab=charts\")\n",
     "\n",
     "# Training setup\n",
     "debug_metrics = {}\n",

From c34dca31bfee3d175de3dc0633b49066d23e753a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 10:02:30 +0100
Subject: [PATCH 065/125] chore: removed unused notebook

---
 .../pytorch_image_classification.ipynb        | 585 ------------------
 1 file changed, 585 deletions(-)
 delete mode 100644 integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb

diff --git a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb b/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
deleted file mode 100644
index 01ca6d4..0000000
--- a/integrations-and-supported-tools/pytorch/pytorch_image_classification.ipynb
+++ /dev/null
@@ -1,585 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Neptune + Pytorch\n",
-    "## Logging and visualizing debugging metrics in PyTorch\n",
-    "\n",
-    "Introduction\n",
-    "\n",
-    "See how Neptune Scale can be used for foundation model traning when you are required to track a large number of metrics across your transformers architecture. \n",
-    "\n",
-    "This guide will show you how to:\n",
-    "- Initialize the Neptune Run object and log configuration parameters\n",
-    "- Log standard loss and accuracy metrics to Neptune\n",
-    "- Log debugging metrics during model training such as;\n",
-    "    * Activations per layer\n",
-    "    * Gradients (mean and std) per layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Before you start\n",
-    "\n",
-    "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
-    "  2. Create a Neptune project that you will use for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
-    "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Install Neptune and Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install dependencies\n",
-    "! pip install -q -U neptune_scale torch torchvision"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO - update config to include model architecture\n",
-    "# TODO - Add more hyperparameters\n",
-    "# TODO - look at CNN layers\n",
-    "# TODO - output and log the model architecture\n",
-    "# TODO - check loss and accuracy calculations\n",
-    "# TODO - clean up the evaluation function to exclude tracking gradients\n",
-    "# TODO - do not use group tags\n",
-    "# TODO - track the input features\n",
-    "# TODO - clean the training loop of commented out code that is unused\n",
-    "# TODO - add batchnormalization and drop out layers to improve the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import torch.nn.functional as F\n",
-    "from torch.utils.data import DataLoader\n",
-    "from torchvision import datasets, transforms\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set Hyperparameters for Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = {\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 256,\n",
-    "    \"learning_rate\": 0.01,\n",
-    "    \"epochs\": 5, \n",
-    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
-    "    \"input_features\": 256,\n",
-    "    \"n_classes\": 10,\n",
-    "    \"input_size\": 28 * 28\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download and transform the data for training\n",
-    "In this example, we will be using the MINST dataset as part of the PyTorch library for illustration. We create a train, validation and test dataset and apply a transformation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 404: Not Found\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 9912422/9912422 [00:11<00:00, 883546.49it/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 404: Not Found\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 28881/28881 [00:00<00:00, 29159.66it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 404: Not Found\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1648877/1648877 [00:01<00:00, 1201389.96it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 404: Not Found\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 4542/4542 [00:00<00:00, 2459084.65it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# Transform to normalize the data and convert it to tensor\n",
-    "transform = transforms.Compose([\n",
-    "    transforms.ToTensor(),\n",
-    "    transforms.Normalize((0.5,), (0.5,))  # Normalizing the image to range [-1, 1]\n",
-    "])\n",
-    "\n",
-    "# Download and load the MNIST dataset\n",
-    "train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
-    "val_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)  # Use test set as validation\n",
-    "test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
-    "\n",
-    "# DataLoader for training, validation, and testing\n",
-    "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
-    "val_loader = DataLoader(val_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n",
-    "test_loader = DataLoader(test_dataset, batch_size=params[\"batch_size\"], shuffle=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Adam\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# Simple Convolutional Neural Network model for MNIST\n",
-    "class SimpleCNN(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(SimpleCNN, self).__init__()\n",
-    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)  # Input channels = 1 (grayscale images)\n",
-    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
-    "        self.fc1 = nn.Linear(64 * 7 * 7, 128)  # Flattened size of image after convolution layers\n",
-    "        self.fc2 = nn.Linear(128, 10)  # 10 output classes for digits 0-9\n",
-    "    \n",
-    "    def forward(self, x):\n",
-    "        x = F.relu(self.conv1(x))\n",
-    "        x = F.max_pool2d(x, 2)  # Pooling layer to downsample\n",
-    "        x = F.relu(self.conv2(x))\n",
-    "        x = F.max_pool2d(x, 2)\n",
-    "        x = x.view(-1, 64 * 7 * 7)  # Flatten the tensor for the fully connected layer\n",
-    "        x = F.relu(self.fc1(x))\n",
-    "        x = self.fc2(x)\n",
-    "        return x\n",
-    "    \n",
-    "class SimpleNN(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(SimpleNN, self).__init__()\n",
-    "        # Define layers (increase number of layers)\n",
-    "        self.fc1 = nn.Linear(params[\"input_size\"], params[\"input_features\"]) \n",
-    "        self.fc2 = nn.Linear(params[\"input_features\"], 512)\n",
-    "        self.fc3 = nn.Linear(512, 256)\n",
-    "        self.fc4 = nn.Linear(256, 128)\n",
-    "        self.fc5 = nn.Linear(128, params[\"n_classes\"])      # Output layer (10 classes for MNIST)\n",
-    "\n",
-    "        # Registering hooks to track activations\n",
-    "        self.hooks = []\n",
-    "        self.hooks.append(self.fc1.register_forward_hook(self.save_activation(\"fc1\")))\n",
-    "        self.hooks.append(self.fc2.register_forward_hook(self.save_activation(\"fc2\")))\n",
-    "        self.hooks.append(self.fc3.register_forward_hook(self.save_activation(\"fc3\")))\n",
-    "        self.hooks.append(self.fc4.register_forward_hook(self.save_activation(\"fc4\")))\n",
-    "        self.hooks.append(self.fc5.register_forward_hook(self.save_activation(\"fc5\")))\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = x.view(-1, params[\"input_size\"])  # Flatten the input image (28x28)\n",
-    "        x = torch.relu(self.fc1(x))  # Apply ReLU activation\n",
-    "        x = torch.relu(self.fc2(x))  # Apply ReLU activation\n",
-    "        x = torch.relu(self.fc3(x))  # Apply ReLU activation\n",
-    "        x = torch.relu(self.fc4(x))  # Apply ReLU activation\n",
-    "        x = self.fc5(x)  # Output layer\n",
-    "        return x\n",
-    "    \n",
-    "        # Function to save activations\n",
-    "    def save_activation(self, name):\n",
-    "        def hook(model, input, output):\n",
-    "            self.activations[name] = output\n",
-    "        return hook\n",
-    "    \n",
-    "    def get_activations(self):\n",
-    "        return self.activations\n",
-    "\n",
-    "    def clear_activations(self):\n",
-    "        self.activations = {}\n",
-    "\n",
-    "# Instantiate the model, loss function, and optimizer\n",
-    "#model = SimpleCNN()\n",
-    "model = SimpleNN()\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "model.to(device)\n",
-    "criterion = nn.CrossEntropyLoss()  # Loss function\n",
-    "\n",
-    "# Select an optimizer\n",
-    "if params[\"optimizer\"] == \"Adam\":\n",
-    "    optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
-    "    print(params[\"optimizer\"])\n",
-    "elif params[\"optimizer\"] == \"SGD\":\n",
-    "    optimizer = optim.SGD(model.parameters(), lr=params[\"learning_rate\"])\n",
-    "    print(params[\"optimizer\"])\n",
-    "else:\n",
-    "    print(\"No optimizer selected\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Function to evaluate the model (validation/test) with gradients tracked\n",
-    "def evaluate(model, data_loader, track_gradients=False):\n",
-    "    model.train() if track_gradients else model.eval()  # Ensure model is in training mode if tracking gradients\n",
-    "    correct_preds = 0\n",
-    "    total_preds = 0\n",
-    "    epoch_loss = 0\n",
-    "    with torch.no_grad():  # Disable gradient tracking during evaluation\n",
-    "        for data, target in data_loader:\n",
-    "            \n",
-    "            data, target = data.to(device), target.to(device)\n",
-    "            \n",
-    "            # Forward pass (with gradient tracking if specified)\n",
-    "            output = model(data)\n",
-    "            loss = criterion(output, target)  # Correct loss computation\n",
-    "            epoch_loss += loss.item()\n",
-    "            \n",
-    "            if track_gradients:\n",
-    "                # Track gradients (we will backpropagate but do not update model parameters)\n",
-    "                loss.backward()\n",
-    "            \n",
-    "            # Calculate accuracy\n",
-    "            _, predicted = torch.max(output.data, 1)\n",
-    "            total_preds += target.size(0)\n",
-    "            correct_preds += (predicted == target).sum().item()\n",
-    "    \n",
-    "    accuracy = correct_preds / total_preds\n",
-    "    return epoch_loss / len(data_loader), accuracy\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Neptune - Initialize Training Run and Log Configs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define Neptune parameters\n",
-    "from neptune_scale import Run\n",
-    "from uuid import uuid4\n",
-    "\n",
-    "run = Run(\n",
-    "    project = \"leo/pytorch-tutorial\",\n",
-    "    run_id=f\"pytorch-{uuid4()}\"\n",
-    "    )\n",
-    "\n",
-    "run.log_configs(\n",
-    "    {\n",
-    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
-    "        \"config/optimizer\": params[\"optimizer\"],\n",
-    "        \"config/batch_size\": params[\"batch_size\"],\n",
-    "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/input_size\": params[\"input_size\"]\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
-    "run.add_tags(tags=[\"Torch-MINST\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Neptune - Log Metrics while Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch [1/5] Training complete. Loss: 0.5882, Accuracy: 0.80%\n",
-      "{'layers/layer_fc1/activation_mean': -28.098386764526367, 'layers/layer_fc4/activation_mean': -28.098386764526367, 'layers/layer_fc5/activation_mean': -28.098386764526367, 'layers/layer_fc2/activation_mean': -4.294741630554199, 'layers/layer_fc3/activation_mean': -4.590151309967041, 'layers/layer_fc1/activation_std': 10.138233184814453, 'layers/layer_fc4/activation_std': 10.138233184814453, 'layers/layer_fc5/activation_std': 10.138233184814453, 'layers/layer_fc2/activation_std': 3.513108730316162, 'layers/layer_fc3/activation_std': 4.694570064544678, 'layers/layer_fc1.weight_mean': -4.2270323319826275e-05, 'layers/layer_fc1.bias_mean': 5.756489190389402e-05, 'layers/layer_fc2.weight_mean': 1.7486901924712583e-05, 'layers/layer_fc2.bias_mean': 5.119257184560411e-05, 'layers/layer_fc3.weight_mean': -6.444352038670331e-06, 'layers/layer_fc3.bias_mean': -3.2650757930241525e-05, 'layers/layer_fc4.weight_mean': 4.735463880933821e-05, 'layers/layer_fc4.bias_mean': 0.00019672312191687524, 'layers/layer_fc5.weight_mean': 2.773958840407431e-11, 'layers/layer_fc5.bias_mean': -3.4924596548080444e-10, 'layers/layer_fc1.weight_std': 0.0018884788732975721, 'layers/layer_fc1.bias_std': 0.0020714548882097006, 'layers/layer_fc2.weight_std': 0.002078161109238863, 'layers/layer_fc2.bias_std': 0.0017285202629864216, 'layers/layer_fc3.weight_std': 0.0006235535256564617, 'layers/layer_fc3.bias_std': 0.0014959467807784677, 'layers/layer_fc4.weight_std': 0.001397876301780343, 'layers/layer_fc4.bias_std': 0.001897347392514348, 'layers/layer_fc5.weight_std': 0.014698885381221771, 'layers/layer_fc5.bias_std': 0.015608142130076885, 'grad_norm/fc1.weight': 0.8462492823600769, 'grad_norm/fc1.bias': 0.03309129923582077, 'grad_norm/fc2.weight': 0.7523983716964722, 'grad_norm/fc2.bias': 0.039090901613235474, 'grad_norm/fc3.weight': 0.22576160728931427, 'grad_norm/fc3.bias': 0.023894065991044044, 'grad_norm/fc4.weight': 0.25318393111228943, 'grad_norm/fc4.bias': 0.021497542038559914, 'grad_norm/fc5.weight': 0.5256778597831726, 'grad_norm/fc5.bias': 0.04682442545890808}\n",
-      "Epoch [2/5] Training complete. Loss: 0.2444, Accuracy: 0.93%\n",
-      "{'layers/layer_fc1/activation_mean': -28.045276641845703, 'layers/layer_fc4/activation_mean': -28.045276641845703, 'layers/layer_fc5/activation_mean': -28.045276641845703, 'layers/layer_fc2/activation_mean': -6.288389682769775, 'layers/layer_fc3/activation_mean': -7.880246162414551, 'layers/layer_fc1/activation_std': 10.427655220031738, 'layers/layer_fc4/activation_std': 10.427655220031738, 'layers/layer_fc5/activation_std': 10.427655220031738, 'layers/layer_fc2/activation_std': 5.016049385070801, 'layers/layer_fc3/activation_std': 7.8910441398620605, 'layers/layer_fc1.weight_mean': -1.8336819266551174e-05, 'layers/layer_fc1.bias_mean': 2.4514503820682876e-05, 'layers/layer_fc2.weight_mean': -4.397415978019126e-06, 'layers/layer_fc2.bias_mean': -5.701838745153509e-06, 'layers/layer_fc3.weight_mean': 3.609377472457709e-06, 'layers/layer_fc3.bias_mean': 2.2656186047242954e-05, 'layers/layer_fc4.weight_mean': 3.2792174806672847e-06, 'layers/layer_fc4.bias_mean': 6.183851655805483e-05, 'layers/layer_fc5.weight_mean': 1.033322427623773e-09, 'layers/layer_fc5.bias_mean': 2.793967834868738e-10, 'layers/layer_fc1.weight_std': 0.0009652073495090008, 'layers/layer_fc1.bias_std': 0.0011003392282873392, 'layers/layer_fc2.weight_std': 0.0015912855742499232, 'layers/layer_fc2.bias_std': 0.0008093378273770213, 'layers/layer_fc3.weight_std': 0.000562622444704175, 'layers/layer_fc3.bias_std': 0.0008122650324366987, 'layers/layer_fc4.weight_std': 0.0013753673993051052, 'layers/layer_fc4.bias_std': 0.001243805163539946, 'layers/layer_fc5.weight_std': 0.02044137567281723, 'layers/layer_fc5.bias_std': 0.01635204255580902, 'grad_norm/fc1.weight': 0.4324897825717926, 'grad_norm/fc1.bias': 0.01757538504898548, 'grad_norm/fc2.weight': 0.57610684633255, 'grad_norm/fc2.bias': 0.018295787274837494, 'grad_norm/fc3.weight': 0.2036944478750229, 'grad_norm/fc3.bias': 0.012975896708667278, 'grad_norm/fc4.weight': 0.24896498024463654, 'grad_norm/fc4.bias': 0.014034420251846313, 'grad_norm/fc5.weight': 0.7310471534729004, 'grad_norm/fc5.bias': 0.04905613139271736}\n",
-      "Epoch [3/5] Training complete. Loss: 0.2124, Accuracy: 0.94%\n",
-      "{'layers/layer_fc1/activation_mean': -28.00644874572754, 'layers/layer_fc4/activation_mean': -28.00644874572754, 'layers/layer_fc5/activation_mean': -28.00644874572754, 'layers/layer_fc2/activation_mean': -8.306803703308105, 'layers/layer_fc3/activation_mean': -10.63320255279541, 'layers/layer_fc1/activation_std': 10.678731918334961, 'layers/layer_fc4/activation_std': 10.678731918334961, 'layers/layer_fc5/activation_std': 10.678731918334961, 'layers/layer_fc2/activation_std': 6.999919891357422, 'layers/layer_fc3/activation_std': 10.323572158813477, 'layers/layer_fc1.weight_mean': 7.430891855619848e-05, 'layers/layer_fc1.bias_mean': -9.825517918216065e-05, 'layers/layer_fc2.weight_mean': 1.702215740806423e-05, 'layers/layer_fc2.bias_mean': 5.076182424090803e-05, 'layers/layer_fc3.weight_mean': 2.006397380682756e-06, 'layers/layer_fc3.bias_mean': 9.8647487902781e-06, 'layers/layer_fc4.weight_mean': 3.732190089067444e-05, 'layers/layer_fc4.bias_mean': 0.00015728663129266351, 'layers/layer_fc5.weight_mean': 5.190377017072478e-10, 'layers/layer_fc5.bias_mean': 7.450580707946131e-10, 'layers/layer_fc1.weight_std': 0.0013835423160344362, 'layers/layer_fc1.bias_std': 0.0014023504918441176, 'layers/layer_fc2.weight_std': 0.0018697652267292142, 'layers/layer_fc2.bias_std': 0.0008941664709709585, 'layers/layer_fc3.weight_std': 0.0007453133002854884, 'layers/layer_fc3.bias_std': 0.0008750202250666916, 'layers/layer_fc4.weight_std': 0.0016579279908910394, 'layers/layer_fc4.bias_std': 0.001322976779192686, 'layers/layer_fc5.weight_std': 0.018788360059261322, 'layers/layer_fc5.bias_std': 0.0166013166308403, 'grad_norm/fc1.weight': 0.620717465877533, 'grad_norm/fc1.bias': 0.02244885452091694, 'grad_norm/fc2.weight': 0.6769528388977051, 'grad_norm/fc2.bias': 0.020245518535375595, 'grad_norm/fc3.weight': 0.26983216404914856, 'grad_norm/fc3.bias': 0.013973843306303024, 'grad_norm/fc4.weight': 0.30018848180770874, 'grad_norm/fc4.bias': 0.015015012584626675, 'grad_norm/fc5.weight': 0.671930193901062, 'grad_norm/fc5.bias': 0.049803949892520905}\n",
-      "Epoch [4/5] Training complete. Loss: 0.1774, Accuracy: 0.95%\n",
-      "{'layers/layer_fc1/activation_mean': -28.02176284790039, 'layers/layer_fc4/activation_mean': -28.02176284790039, 'layers/layer_fc5/activation_mean': -28.02176284790039, 'layers/layer_fc2/activation_mean': -9.155248641967773, 'layers/layer_fc3/activation_mean': -13.8362455368042, 'layers/layer_fc1/activation_std': 10.731369972229004, 'layers/layer_fc4/activation_std': 10.731369972229004, 'layers/layer_fc5/activation_std': 10.731369972229004, 'layers/layer_fc2/activation_std': 7.8137617111206055, 'layers/layer_fc3/activation_std': 12.559845924377441, 'layers/layer_fc1.weight_mean': 4.3527179514057934e-05, 'layers/layer_fc1.bias_mean': -5.993247759761289e-05, 'layers/layer_fc2.weight_mean': -8.3655349953915e-06, 'layers/layer_fc2.bias_mean': -2.7970250812359154e-05, 'layers/layer_fc3.weight_mean': -6.794801265641581e-06, 'layers/layer_fc3.bias_mean': -5.020684693590738e-05, 'layers/layer_fc4.weight_mean': -5.56191080249846e-05, 'layers/layer_fc4.bias_mean': -0.00022704749426338822, 'layers/layer_fc5.weight_mean': -4.933287667263642e-10, 'layers/layer_fc5.bias_mean': -1.0710209386033398e-09, 'layers/layer_fc1.weight_std': 0.0006279172375798225, 'layers/layer_fc1.bias_std': 0.0006310796015895903, 'layers/layer_fc2.weight_std': 0.0009842520812526345, 'layers/layer_fc2.bias_std': 0.00036153788096271455, 'layers/layer_fc3.weight_std': 0.0003291342291049659, 'layers/layer_fc3.bias_std': 0.0003552739799488336, 'layers/layer_fc4.weight_std': 0.0006492410320788622, 'layers/layer_fc4.bias_std': 0.0005454771453514695, 'layers/layer_fc5.weight_std': 0.0076590655371546745, 'layers/layer_fc5.bias_std': 0.005646508652716875, 'grad_norm/fc1.weight': 0.28198111057281494, 'grad_norm/fc1.bias': 0.010123053565621376, 'grad_norm/fc2.weight': 0.35634881258010864, 'grad_norm/fc2.bias': 0.008197144605219364, 'grad_norm/fc3.weight': 0.119184210896492, 'grad_norm/fc3.bias': 0.005729861091822386, 'grad_norm/fc4.weight': 0.11795385181903839, 'grad_norm/fc4.bias': 0.006662336643785238, 'grad_norm/fc5.weight': 0.2739119827747345, 'grad_norm/fc5.bias': 0.016939526423811913}\n",
-      "Epoch [5/5] Training complete. Loss: 0.1836, Accuracy: 0.95%\n",
-      "{'layers/layer_fc1/activation_mean': -27.910594940185547, 'layers/layer_fc4/activation_mean': -27.910594940185547, 'layers/layer_fc5/activation_mean': -27.910594940185547, 'layers/layer_fc2/activation_mean': -11.856008529663086, 'layers/layer_fc3/activation_mean': -16.673297882080078, 'layers/layer_fc1/activation_std': 11.18954086303711, 'layers/layer_fc4/activation_std': 11.18954086303711, 'layers/layer_fc5/activation_std': 11.18954086303711, 'layers/layer_fc2/activation_std': 10.051806449890137, 'layers/layer_fc3/activation_std': 14.125566482543945, 'layers/layer_fc1.weight_mean': -4.3949068640358746e-05, 'layers/layer_fc1.bias_mean': 5.6177024816861376e-05, 'layers/layer_fc2.weight_mean': -3.007975647051353e-05, 'layers/layer_fc2.bias_mean': -6.0376849432941526e-05, 'layers/layer_fc3.weight_mean': -6.851441867183894e-06, 'layers/layer_fc3.bias_mean': -4.150162567384541e-05, 'layers/layer_fc4.weight_mean': -3.618385744630359e-05, 'layers/layer_fc4.bias_mean': -9.973671694751829e-05, 'layers/layer_fc5.weight_mean': 1.3166733259240004e-09, 'layers/layer_fc5.bias_mean': 1.3737008197622913e-09, 'layers/layer_fc1.weight_std': 0.0014514160575345159, 'layers/layer_fc1.bias_std': 0.001587417908012867, 'layers/layer_fc2.weight_std': 0.002324556466192007, 'layers/layer_fc2.bias_std': 0.000764612341299653, 'layers/layer_fc3.weight_std': 0.0007226017769426107, 'layers/layer_fc3.bias_std': 0.0007500615902245045, 'layers/layer_fc4.weight_std': 0.001422140165232122, 'layers/layer_fc4.bias_std': 0.000992378918454051, 'layers/layer_fc5.weight_std': 0.016280390322208405, 'layers/layer_fc5.bias_std': 0.013839689083397388, 'grad_norm/fc1.weight': 0.6505305767059326, 'grad_norm/fc1.bias': 0.025364963337779045, 'grad_norm/fc2.weight': 0.8416466116905212, 'grad_norm/fc2.bias': 0.017338205128908157, 'grad_norm/fc3.weight': 0.26162052154541016, 'grad_norm/fc3.bias': 0.01199591625481844, 'grad_norm/fc4.weight': 0.25751423835754395, 'grad_norm/fc4.bias': 0.011240324936807156, 'grad_norm/fc5.weight': 0.5822373628616333, 'grad_norm/fc5.bias': 0.04151906818151474}\n",
-      "Testing complete. Loss: 0.1949, Accuracy: 0.95%\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Training loop\n",
-    "activation_dict_mean = {}\n",
-    "activation_dict_std = {}\n",
-    "params_dict_std = {}\n",
-    "params_dict_mean = {}\n",
-    "grad_norms = {}\n",
-    "\n",
-    "num_epochs = params[\"epochs\"]\n",
-    "step_counter = 0\n",
-    "for epoch in range(num_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "    correct_preds = 0\n",
-    "    total_preds = 0\n",
-    "\n",
-    "    # Reset activations for each epoch\n",
-    "    model.clear_activations()\n",
-    "    \n",
-    "    # Training step\n",
-    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
-    "        step_counter += 1\n",
-    "        optimizer.zero_grad()\n",
-    "        \n",
-    "        data, target = data.to(device), target.to(device)\n",
-    "\n",
-    "        # Forward pass\n",
-    "        output = model(data)\n",
-    "        \n",
-    "        # Compute the loss\n",
-    "        loss = criterion(output, target)\n",
-    "        \n",
-    "        # Backward pass and optimization\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        epoch_loss += loss.item()\n",
-    "        \n",
-    "        # Calculate accuracy\n",
-    "        _, predicted = torch.max(output.data, 1)\n",
-    "        total_preds += target.size(0)\n",
-    "        correct_preds += (predicted == target).sum().item()\n",
-    "        \n",
-    "        # Print loss and accuracy for each batch (step)\n",
-    "        #if (batch_idx + 1) % 5 == 0:  # Every 5 steps\n",
-    "        batch_accuracy = correct_preds / total_preds\n",
-    "        # print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}, Accuracy: {batch_accuracy:.2f}%\")\n",
-    "                \n",
-    "        # Validation step per training step\n",
-    "        val_loss, val_accuracy = evaluate(model, val_loader)  # Evaluate after each step\n",
-    "        # print(f\"Validation at step [{batch_idx+1}/{len(train_loader)}] - Loss: {val_loss:.4f}, Accuracy: {val_accuracy:.2f}%\")\n",
-    "\n",
-    "        for name, param in model.named_parameters():\n",
-    "            if param is not None:\n",
-    "                grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
-    "\n",
-    "        run.log_metrics(\n",
-    "            data = {\n",
-    "                \"metrics/train/loss\": loss.item(),\n",
-    "                \"metrics/train/accuracy\": batch_accuracy,\n",
-    "                \"metrics/validation/loss\": val_loss,\n",
-    "                \"metrics/validation/accuracy\": val_accuracy,\n",
-    "                \"epoch_value\": epoch,\n",
-    "                **grad_norms\n",
-    "            },\n",
-    "            step = step_counter\n",
-    "        )\n",
-    "    \n",
-    "    # Print loss and accuracy for the entire training epoch\n",
-    "    train_accuracy = correct_preds / total_preds\n",
-    "    print(f\"Epoch [{epoch+1}/{num_epochs}] Training complete. Loss: {epoch_loss / len(train_loader):.4f}, Accuracy: {train_accuracy:.2f}%\")\n",
-    "\n",
-    "    # Track activations\n",
-    "    for name, activation in model.get_activations().items():\n",
-    "        activation_dict_mean[f\"layers/layer_{name}/activation_mean\"] = activation.mean().item()\n",
-    "        activation_dict_std[f\"layers/layer_{name}/activation_std\"] = activation.std().item()\n",
-    "\n",
-    "    # Track gradients and norms per layer at each epoch\n",
-    "    for name, param in model.named_parameters():\n",
-    "        if param is not None:\n",
-    "            params_dict_std[f\"layers/layer_{name}_std\"] = param.grad.std().item()\n",
-    "            params_dict_mean[f\"layers/layer_{name}_mean\"] = param.grad.mean().item()\n",
-    "            # grad_norms[f\"grad_norm/{name}\"] = param.grad.norm(2).item() # L2 norm (Euclidean norm) of the gradients\n",
-    "    \n",
-    "    layers_dict = {**activation_dict_mean, \n",
-    "                   **activation_dict_std,\n",
-    "                   **params_dict_mean,\n",
-    "                   **params_dict_std,\n",
-    "                   **grad_norms\n",
-    "                   }\n",
-    "    print(layers_dict)\n",
-    "\n",
-    "    # data_to_log = {\n",
-    "    #        \"metrics/test/loss_epoch\": epoch_loss / len(train_loader),\n",
-    "     #       \"metrics/train/accuracy_epoch\": train_accuracy\n",
-    "      #  }.update(activation_dict)\n",
-    "    \n",
-    "    run.log_metrics(\n",
-    "    data = layers_dict,\n",
-    "    step = epoch\n",
-    "    )\n",
-    "    \n",
-    "# Final Testing Step with gradient tracking\n",
-    "test_loss, test_accuracy = evaluate(model, test_loader, track_gradients=False)  # Track gradients during test\n",
-    "print(f\"Testing complete. Loss: {test_loss:.4f}, Accuracy: {test_accuracy:.2f}%\")\n",
-    "\n",
-    "run.log_configs(\n",
-    "        {\n",
-    "        \"metrics/test/loss\": test_loss,\n",
-    "        \"metrics/test/accuracy\": test_accuracy\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "neptune:INFO: Waiting for all operations to be processed\n",
-      "neptune:WARNING: No timeout specified. Waiting indefinitely\n",
-      "neptune:INFO: All operations were processed\n"
-     ]
-    }
-   ],
-   "source": [
-    "run.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "neptune_scale_py_312_base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 5fd0a0e5148ee6817222606bf015ac6b1d8e8fef Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 10:04:42 +0100
Subject: [PATCH 066/125] chore: clean up comments and code cell outputs

---
 .../pytorch_text_model_debugging.ipynb        | 31 +++----------------
 1 file changed, 4 insertions(+), 27 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
index 106c3db..7f99bae 100644
--- a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
@@ -63,18 +63,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -330,17 +321,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "View current experiment: https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?viewId=standard-view&runIdentificationKey=pytorch-text&type=experiment\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define Neptune parameters\n",
     "from neptune_scale import Run\n",
@@ -444,9 +427,6 @@
     "        gradients = hook_manager.get_gradients()\n",
     "        for layer, gradient in gradients.items():\n",
     "            debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
-    "            print(f\"Gradients for {layer}: {gradient.mean().item()}\") # You can replace to use mean(), sum(), max() or min()\n",
-    "            # simplified_gradient = gradient.mean(dim=(0, 1))\n",
-    "            # print(f\"Summed Gradient for {layer}: {simplified_gradient}\")\n",
     "\n",
     "        # Track gradients per layer at each epoch\n",
     "        for layer, param in model.named_parameters():\n",
@@ -479,9 +459,6 @@
     "        \n",
     "    print(f\"Epoch {epoch + 1}, Loss: {total_loss / len(train_dataloader)}\")\n",
     "\n",
-    "# test_loss = evaluate_model(model, test_input, test_target, params[\"vocab_size\"])\n",
-    "# print(f'Test Loss: {test_loss:.4f}')\n",
-    "\n",
     "# Close run to ensure all operations are processed\n",
     "run.close()"
    ]

From 23ea6635d65125ce7ed616cd9e7b34d7fa1b8500 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 10:06:50 +0100
Subject: [PATCH 067/125] chore: move notebook into its own notebook folder

---
 .../pytorch/{ => notebooks}/pytorch_text_model_debugging.ipynb    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename integrations-and-supported-tools/pytorch/{ => notebooks}/pytorch_text_model_debugging.ipynb (100%)

diff --git a/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
similarity index 100%
rename from integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb
rename to integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb

From 21114f643b3c92280acfe6d1dad98d7dadc4fc5a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 10:07:31 +0100
Subject: [PATCH 068/125] chore: fix comment

---
 .../pytorch/notebooks/pytorch_text_model_debugging.ipynb        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 7f99bae..9cec929 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -435,7 +435,7 @@
     "                debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
     "                debug_metrics[f\"debug/parameters/{layer}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
     "\n",
-    "        if step_counter % 50 == 0: # Log validation loss at every 30 steps\n",
+    "        if step_counter % 50 == 0: # Log validation loss at every 50 steps\n",
     "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",
     "\n",
     "            run.log_metrics(\n",

From c8cd6768c174a56581eedd812c5ac944fd1ed2e6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 11:00:47 +0100
Subject: [PATCH 069/125] refactor: update notebook to use envionment variables

---
 .../pytorch_text_model_debugging.ipynb        | 31 ++++++++++++++++---
 1 file changed, 26 insertions(+), 5 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 9cec929..1aa1d62 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -44,6 +44,30 @@
     "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set environment variables\n",
+    "Once you have your project and workspace names as well as your API token from the UI, you can set them as environment variables to be used through this notebook. Uncomment the code block below and replace with your own details.\n",
+    "```python \n",
+    "# Set Neptune credentials as environment variables\n",
+    "%env NEPTUNE_API_TOKEN = \"your_api_token_here\"\n",
+    "%env NEPTUNE_PROJECT = \"your_workspace_name_here/your_project_name_here\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set Neptune credentials as environment variables\n",
+    "# %env NEPTUNE_API_TOKEN = \"your_api_token_here\"\n",
+    "# %env NEPTUNE_PROJECT = \"your_workspace_name_here/your_project_name_here\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -325,16 +349,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define Neptune parameters\n",
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
     "\n",
-    "project = \"leo/pytorch-tutorial\"\n",
     "custom_run_id = f\"pytorch-text-{uuid4()}\" # Create your own custom run_id\n",
     "experiment_name = \"pytorch-text\" # Create a run that is the head of an experiment. This will also be used for forking.\n",
     "\n",
     "run = Run(\n",
-    "    project = project,\n",
     "    run_id=custom_run_id,\n",
     "    experiment_name = experiment_name,\n",
     "    )\n",
@@ -354,7 +375,7 @@
     "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])\n",
     "\n",
-    "print(f\"View current experiment: https://scale.neptune.ai/{project}/runs/details?viewId=standard-view&runIdentificationKey={experiment_name}&type=experiment\")"
+    "print(f\"View current experiment: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/runs/details?viewId=standard-view&runIdentificationKey={experiment_name}&type=experiment\")"
    ]
   },
   {
@@ -372,7 +393,7 @@
    "outputs": [],
    "source": [
     "# Create links to access active training run. Note - this is not required for the example to run, but for ease of illustration of the tutorial.\n",
-    "print(f\"View training charts: https://scale.neptune.ai/{project}/-/run/?customId={custom_run_id}&detailsTab=charts\")\n",
+    "print(f\"View training charts: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/-/run/?customId={custom_run_id}&detailsTab=charts\")\n",
     "\n",
     "# Training setup\n",
     "debug_metrics = {}\n",

From fe4c1a38c98098670c7e154fdc04a47c5fde70fd Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 11:02:35 +0100
Subject: [PATCH 070/125] chore: update colab link

---
 .../pytorch/notebooks/pytorch_text_model_debugging.ipynb        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 1aa1d62..c33a9c8 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Neptune + PyTorch\n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lr%2Fpytorch_example/integrations-and-supported-tools/pytorch/pytorch_text_model_debugging.ipynb\"> \n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lr%2Fpytorch_example/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
     "</a>\n",
     "\n",

From 6e96c5cc990694c4be7bee904fb8535364fc6969 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 11:35:52 +0100
Subject: [PATCH 071/125] chore: accpet pre-commit recommendations

---
 .../pytorch_text_model_debugging.ipynb        | 152 +++++++++---------
 1 file changed, 77 insertions(+), 75 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index c33a9c8..1939444 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,13 +123,13 @@
     "    \"optimizer\": \"Adam\",\n",
     "    \"batch_size\": 8,\n",
     "    \"learning_rate\": 0.01,\n",
-    "    \"epochs\": 5, \n",
+    "    \"epochs\": 5,\n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
     "    \"embed_size\": 1000,\n",
-    "    \"hidden_size\": 256, # hidden size for the LSTM\n",
+    "    \"hidden_size\": 256,  # hidden size for the LSTM\n",
     "    \"dropout_prob\": 0.3,\n",
-    "    \"num_lstm_layers\": 3\n",
+    "    \"num_lstm_layers\": 3,\n",
     "}"
    ]
   },
@@ -143,28 +143,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 81926 \n",
-      "Validation samples: 935\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
     "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
-    "data_files = {\"train\": base_url + \"train-00001-of-00067.parquet\", # download only the first 10 files from the HF dataset\n",
-    "              \"validation\": base_url + \"validation-00000-of-00001.parquet\"} #doanload the complete validation dataset\n",
+    "data_files = {\n",
+    "    \"train\": base_url\n",
+    "    + \"train-00001-of-00067.parquet\",  # download only the first 10 files from the HF dataset\n",
+    "    \"validation\": base_url + \"validation-00000-of-00001.parquet\",\n",
+    "}  # doanload the complete validation dataset\n",
     "\n",
-    "data_subset = load_dataset(\"parquet\", data_files = data_files, num_proc=4)\n",
+    "data_subset = load_dataset(\"parquet\", data_files=data_files, num_proc=4)\n",
     "# validation_subset = load_dataset(\"parquet\", data_files = {\"validation\": base_url + \"validation-00000-of-00001.parquet\"}, num_proc=4, split=[\"validation[:5%]\"])\n",
     "validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
-    "print(f\"Training samples: {data_subset['train'].num_rows} \\nValidation samples: {validation_subset['test'].num_rows}\")"
+    "print(\n",
+    "    f\"Training samples: {data_subset['train'].num_rows} \\nValidation samples: {validation_subset['test'].num_rows}\"\n",
+    ")"
    ]
   },
   {
@@ -178,27 +174,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vocabulary size: 128257\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train_subset = data_subset[\"train\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
-    "validation_subset = validation_subset[\"test\"].with_format(type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]) # HF provides methods to convert datatypes to tensors\n",
+    "train_subset = data_subset[\"train\"].with_format(\n",
+    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
+    ")  # HF provides methods to convert datatypes to tensors\n",
+    "validation_subset = validation_subset[\"test\"].with_format(\n",
+    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
+    ")  # HF provides methods to convert datatypes to tensors\n",
     "\n",
     "train_dataloader = DataLoader(train_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
     "val_dataloader = DataLoader(validation_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
     "\n",
     "# Determine the vocab size of the dataset\n",
     "# Flatten the list of tokenized sentences into one long list of token IDs\n",
-    "vocab_size = max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
+    "vocab_size = (\n",
+    "    max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
+    ")\n",
     "params[\"vocab_size\"] = vocab_size\n",
     "print(f\"Vocabulary size: {vocab_size}\")"
    ]
@@ -213,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,27 +216,28 @@
     "    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):\n",
     "        super(SimpleLLM, self).__init__()\n",
     "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers = num_layers, batch_first=True)\n",
+    "        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers=num_layers, batch_first=True)\n",
     "        self.fc1 = nn.Linear(hidden_size, vocab_size)\n",
-    "        \n",
+    "\n",
     "    def forward(self, x):\n",
     "        x = self.embedding(x)\n",
     "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
-    "        out = self.fc1(lstm_out) # Use the last output from the LSTM\n",
+    "        out = self.fc1(lstm_out)  # Use the last output from the LSTM\n",
     "        return out\n",
     "\n",
+    "\n",
     "# Function to evaluate the model after each epoch/step\n",
     "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
     "    model.eval()  # Set the model to evaluation mode\n",
     "    total_loss = 0\n",
     "    with torch.no_grad():  # Disable gradient calculation for validation\n",
     "        for batch in val_dataloader:\n",
-    "            input_ids = batch['input_ids'].to(device)\n",
-    "            labels = batch['labels'].to(device)\n",
+    "            input_ids = batch[\"input_ids\"].to(device)\n",
+    "            labels = batch[\"labels\"].to(device)\n",
     "\n",
     "            # Forward pass for validation\n",
     "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
-    "            \n",
+    "\n",
     "            # Calculate the loss\n",
     "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
     "            total_loss += loss.item()\n",
@@ -283,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,7 +287,7 @@
     "    def __init__(self, model):\n",
     "        if not isinstance(model, nn.Module):\n",
     "            raise TypeError(\"The model must be a PyTorch model\")\n",
-    "        \n",
+    "\n",
     "        self.model = model\n",
     "        self.hooks = []\n",
     "        self.activations = {}\n",
@@ -302,12 +297,14 @@
     "    def save_activation(self, name):\n",
     "        def hook(module, input, output):\n",
     "            self.activations[name] = output\n",
+    "\n",
     "        return hook\n",
     "\n",
     "    # Function to save gradients (registering hooks for the model parameters)\n",
     "    def save_gradient(self, name):\n",
     "        def hook(module, grad_input, grad_output):\n",
     "            self.gradients[name] = grad_output[0]\n",
+    "\n",
     "        return hook\n",
     "\n",
     "    # Function to register hooks for activations and gradients\n",
@@ -318,7 +315,7 @@
     "\n",
     "        # Register backward hooks for gradients\n",
     "        for name, module in self.model.named_modules():\n",
-    "            if isinstance(module, (nn.LSTM, nn.Linear)): # You can add more layer types here\n",
+    "            if isinstance(module, (nn.LSTM, nn.Linear)):  # You can add more layer types here\n",
     "                self.hooks.append(module.register_full_backward_hook(self.save_gradient(name)))\n",
     "\n",
     "    # Function to clear activations and gradients after use\n",
@@ -352,13 +349,13 @@
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
     "\n",
-    "custom_run_id = f\"pytorch-text-{uuid4()}\" # Create your own custom run_id\n",
-    "experiment_name = \"pytorch-text\" # Create a run that is the head of an experiment. This will also be used for forking.\n",
+    "custom_run_id = f\"pytorch-text-{uuid4()}\"  # Create your own custom run_id\n",
+    "experiment_name = \"pytorch-text\"  # Create a run that is the head of an experiment. This will also be used for forking.\n",
     "\n",
     "run = Run(\n",
     "    run_id=custom_run_id,\n",
-    "    experiment_name = experiment_name,\n",
-    "    )\n",
+    "    experiment_name=experiment_name,\n",
+    ")\n",
     "\n",
     "run.log_configs(\n",
     "    {\n",
@@ -366,16 +363,18 @@
     "        \"config/optimizer\": params[\"optimizer\"],\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/num_lstm_layers\" : params[\"num_lstm_layers\"],\n",
+    "        \"config/num_lstm_layers\": params[\"num_lstm_layers\"],\n",
     "        \"data/vocab_size\": params[\"vocab_size\"],\n",
-    "        \"data/embed_size\": params[\"embed_size\"]\n",
+    "        \"data/embed_size\": params[\"embed_size\"],\n",
     "    }\n",
     ")\n",
     "\n",
     "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])\n",
     "\n",
-    "print(f\"View current experiment: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/runs/details?viewId=standard-view&runIdentificationKey={experiment_name}&type=experiment\")"
+    "print(\n",
+    "    f\"View current experiment: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/runs/details?viewId=standard-view&runIdentificationKey={experiment_name}&type=experiment\"\n",
+    ")"
    ]
   },
   {
@@ -393,20 +392,26 @@
    "outputs": [],
    "source": [
     "# Create links to access active training run. Note - this is not required for the example to run, but for ease of illustration of the tutorial.\n",
-    "print(f\"View training charts: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/-/run/?customId={custom_run_id}&detailsTab=charts\")\n",
+    "print(\n",
+    "    f\"View training charts: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/-/run/?customId={custom_run_id}&detailsTab=charts\"\n",
+    ")\n",
     "\n",
     "# Training setup\n",
     "debug_metrics = {}\n",
     "\n",
     "# Initialize model and optimizer\n",
-    "model = SimpleLLM(params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"])\n",
-    "optimizer = optim.Adam(model.parameters(), lr = params[\"learning_rate\"])\n",
-    "criterion = nn.CrossEntropyLoss(ignore_index=-100) # Ignore the buffering index of -100 in the dataset\n",
+    "model = SimpleLLM(\n",
+    "    params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"]\n",
+    ")\n",
+    "optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
+    "criterion = nn.CrossEntropyLoss(\n",
+    "    ignore_index=-100\n",
+    ")  # Ignore the buffering index of -100 in the dataset\n",
     "\n",
     "hook_manager = HookManager(model)\n",
     "hook_manager.register_hooks()\n",
     "\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model.to(device)\n",
     "step_counter = 0\n",
     "\n",
@@ -418,22 +423,21 @@
     "        step_counter += 1\n",
     "        hook_manager.clear()\n",
     "\n",
-    "        input_ids = batch['input_ids'].to(device)\n",
-    "        labels = batch['labels'].to(device)\n",
-    "        \n",
+    "        input_ids = batch[\"input_ids\"].to(device)\n",
+    "        labels = batch[\"labels\"].to(device)\n",
+    "\n",
     "        optimizer.zero_grad()\n",
-    "        \n",
+    "\n",
     "        # Forward pass\n",
     "        logits = model(input_ids)\n",
-    "        \n",
+    "\n",
     "        # Compute the loss (ignore padding tokens by masking labels)\n",
     "        loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "        \n",
+    "\n",
     "        # Backward pass and optimization\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
     "\n",
-    "        \n",
     "        total_loss += loss.item()\n",
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
     "\n",
@@ -454,30 +458,28 @@
     "            if param is not None:\n",
     "                debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
     "                debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
-    "                debug_metrics[f\"debug/parameters/{layer}_norm\"] = param.grad.norm().item() # L2 norm (Euclidean norm) of the gradients\n",
+    "                debug_metrics[f\"debug/parameters/{layer}_norm\"] = (\n",
+    "                    param.grad.norm().item()\n",
+    "                )  # L2 norm (Euclidean norm) of the gradients\n",
     "\n",
-    "        if step_counter % 50 == 0: # Log validation loss at every 50 steps\n",
+    "        if step_counter % 50 == 0:  # Log validation loss at every 50 steps\n",
     "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",
     "\n",
     "            run.log_metrics(\n",
-    "                data = {\n",
+    "                data={\n",
     "                    \"metrics/train/loss\": loss.item(),\n",
     "                    \"metrics/validation/loss\": val_loss,\n",
     "                    \"epoch/value\": epoch,\n",
-    "                    **debug_metrics\n",
+    "                    **debug_metrics,\n",
     "                },\n",
-    "                step = step_counter\n",
+    "                step=step_counter,\n",
     "            )\n",
-    "        else: # Log training loss and debugging metrics for each step\n",
+    "        else:  # Log training loss and debugging metrics for each step\n",
     "            run.log_metrics(\n",
-    "                data = {\n",
-    "                    \"metrics/train/loss\": loss.item(),\n",
-    "                    \"epoch/value\": epoch,\n",
-    "                    **debug_metrics\n",
-    "                },\n",
-    "                step = step_counter\n",
+    "                data={\"metrics/train/loss\": loss.item(), \"epoch/value\": epoch, **debug_metrics},\n",
+    "                step=step_counter,\n",
     "            )\n",
-    "        \n",
+    "\n",
     "    print(f\"Epoch {epoch + 1}, Loss: {total_loss / len(train_dataloader)}\")\n",
     "\n",
     "# Close run to ensure all operations are processed\n",

From b16869b4cb8a32ddc60eb6e954982214f6daca1e Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 20 Mar 2025 11:42:32 +0100
Subject: [PATCH 072/125] chore: add notebook to test workflow

---
 .github/workflows/test-notebooks.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/test-notebooks.yml b/.github/workflows/test-notebooks.yml
index 016c353..06d5be7 100644
--- a/.github/workflows/test-notebooks.yml
+++ b/.github/workflows/test-notebooks.yml
@@ -21,6 +21,7 @@ jobs:
         python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
         notebooks: # Add in alphabetical order
           - how-to-guides/hpo/notebooks/Neptune_HPO.ipynb
+          - integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
         os: ["${{ inputs.os }}"]
     steps:
       - uses: actions/checkout@main

From 15357bb749b81b607c2ba99d75c9ac8cc058fe87 Mon Sep 17 00:00:00 2001
From: szaganek <szaganek@gmail.com>
Date: Thu, 27 Mar 2025 12:23:26 +0100
Subject: [PATCH 073/125] TW review

---
 .../pytorch_text_model_debugging.ipynb        | 121 +++++++++++-------
 1 file changed, 78 insertions(+), 43 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 1939444..31167f5 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -8,29 +8,47 @@
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lr%2Fpytorch_example/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
-    "</a>\n",
-    "\n",
-    "## Logging and visualizing debugging metrics with Neptune\n",
-    "Global metrics (e.g., loss, accuracy) provide a high-level performance snapshot and ensure training is on course. However, for large or foundation models, monitoring layer-wise metrics—such as gradients and activations—delivers critical insights into how each layer learns. This level of detail helps identify issues (e.g., vanishing/exploding gradients) and fine-tune individual layers for better overall performance.\n",
-    "The **main challenge** is the sheer volume of data generated by layer-wise logging. Fortunately, Neptune is built for hyperscale tracking, enabling you to efficiently capture, organize, and analyze metrics from every layer—no matter how large your model—without disrupting the training process.\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "Global metrics such as loss or accuracy provide a high-level performance snapshot and ensure training is on course.\n",
     "\n",
-    "### Introduction\n",
-    "This example is designed to be used as a code recipe for you to re-use sections with your own code to edit or adapt to your own model training needs. \n",
+    "However, for large or foundation models, monitoring layer-wise metrics—such as gradients and activations—delivers critical insights into how each layer learns. This level of detail helps identify issues and fine-tune individual layers for better overall performance.\n",
+    "The main challenge is the volume of data generated by layer-wise logging.\n",
     "\n",
+    "Fortunately, Neptune is built for hyperscale tracking. It enables you to capture, organize, and analyze metrics from every layer without disrupting the training process. No matter how large is your model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "This guide will show you how to:\n",
     "- Initialize the **Neptune Run** object and log configuration parameters\n",
-    "- Create a **reuseable class** to hook layer-wise metrics (`HookManager`)\n",
+    "- Create a **reusable class** to hook layer-wise metrics (`HookManager`)\n",
     "- Log **aggregated metrics** such as loss and accuracy\n",
-    "- Log **layer-wise metrics** to debug model traing such as;\n",
+    "- Log **layer-wise metrics** to debug model training such as:\n",
     "\n",
-    "| **Metric**                        | **Demonstrated** | **What it Shows**                                                                                             | **How to Capture**                                             |\n",
+    "| **Metric**                        | **Demonstrated** | **What it shows**                                                                                             | **How to capture**                                             |\n",
     "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
     "| **Activations**                   | Yes                                  | Dead or exploding activations can indicate issues with training stability. | `HookManager`            |\n",
     "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | `HookManager`        |\n",
     "| **Parameters**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
     "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
-    "| **Learning Rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
-    "| **Output Norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |\n"
+    "| **Learning rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
+    "| **Output norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use this notebook as a code recipe. Add your own code and adapt the sections to your own model training needs."
    ]
   },
   {
@@ -40,7 +58,7 @@
     "## Before you start\n",
     "\n",
     "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
-    "  2. Create a Neptune project that you will use for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
+    "  2. Create a Neptune project for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
     "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
    ]
   },
@@ -49,12 +67,9 @@
    "metadata": {},
    "source": [
     "### Set environment variables\n",
-    "Once you have your project and workspace names as well as your API token from the UI, you can set them as environment variables to be used through this notebook. Uncomment the code block below and replace with your own details.\n",
-    "```python \n",
-    "# Set Neptune credentials as environment variables\n",
-    "%env NEPTUNE_API_TOKEN = \"your_api_token_here\"\n",
-    "%env NEPTUNE_PROJECT = \"your_workspace_name_here/your_project_name_here\"\n",
-    "```"
+    "By setting your project name and API token as environment variables, you can use them throughout this notebook.\n",
+    "\n",
+    "Uncomment the code block below and replace placeholder values with your own credentials:"
    ]
   },
   {
@@ -64,8 +79,8 @@
    "outputs": [],
    "source": [
     "# Set Neptune credentials as environment variables\n",
-    "# %env NEPTUNE_API_TOKEN = \"your_api_token_here\"\n",
-    "# %env NEPTUNE_PROJECT = \"your_workspace_name_here/your_project_name_here\""
+    "# %env NEPTUNE_API_TOKEN = \"your_api_token\"\n",
+    "# %env NEPTUNE_PROJECT = \"your_workspace_name_here/your_project_name\""
    ]
   },
   {
@@ -138,7 +153,11 @@
    "metadata": {},
    "source": [
     "## Download or use next token prediction dataset\n",
-    "The dataset used in this example is taken from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset). In this example, you can increase the size of the dataset to test the logging capabilities of Neptune, but note that increasing the dataset size will increase the time taken for the full dataset to download. The current setup only downloads the first parquet file from the Hugging Face public dataset. The validation dataset is also reduced to reduce the training loop execution time - you can increase the validation size by changing the `test_size` key-value pair in the `train_test_split()` method from HF. "
+    "This example uses the dataset from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset) (HF).\n",
+    "\n",
+    "You can increase the size of the dataset to test the logging capabilities of Neptune. Note that increasing the size will increase the time needed for the dataset to download. The current setup only downloads the first parquet file from the Hugging Face public dataset.\n",
+    "\n",
+    "The validation dataset is also reduced to decrease the training loop execution time. To increase the validation size, change the `test_size` key-value pair in the `train_test_split()` method from HuggingFace."
    ]
   },
   {
@@ -153,7 +172,7 @@
     "    \"train\": base_url\n",
     "    + \"train-00001-of-00067.parquet\",  # download only the first 10 files from the HF dataset\n",
     "    \"validation\": base_url + \"validation-00000-of-00001.parquet\",\n",
-    "}  # doanload the complete validation dataset\n",
+    "}  # download the complete validation dataset\n",
     "\n",
     "data_subset = load_dataset(\"parquet\", data_files=data_files, num_proc=4)\n",
     "# validation_subset = load_dataset(\"parquet\", data_files = {\"validation\": base_url + \"validation-00000-of-00001.parquet\"}, num_proc=4, split=[\"validation[:5%]\"])\n",
@@ -168,8 +187,9 @@
    "metadata": {},
    "source": [
     "## Create `DataLoader` objects\n",
-    "* To execute the models with PyTorch, we convert the training and validation datasets to tensors and then setup DataLoader for easier batching in our training loop.\n",
-    "* The model architecture requires the vocabulary size as an input and this we calculate the max token from the dataset."
+    "To execute the models with PyTorch, convert the training and validation datasets to tensors. Then, set up `DataLoader` for easier batching in the training loop.\n",
+    "\n",
+    "The model architecture requires the vocabulary size as an input and this is why we calculate the max token from the dataset."
    ]
   },
   {
@@ -180,10 +200,10 @@
    "source": [
     "train_subset = data_subset[\"train\"].with_format(\n",
     "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
-    ")  # HF provides methods to convert datatypes to tensors\n",
+    ")  # HF provides methods to convert data types to tensors\n",
     "validation_subset = validation_subset[\"test\"].with_format(\n",
     "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
-    ")  # HF provides methods to convert datatypes to tensors\n",
+    ")  # HF provides methods to convert data types to tensors\n",
     "\n",
     "train_dataloader = DataLoader(train_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
     "val_dataloader = DataLoader(validation_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
@@ -202,7 +222,11 @@
    "metadata": {},
    "source": [
     "### Define PyTorch model architecture and helpers\n",
-    "We define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer and a fully connected layer. This architecture can be adjusted to your needs and increased in size when testing the workflow. To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary or to increase the number of fully connected layers, update the mode architecture itself."
+    "Define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer, and a fully connected layer.\n",
+    "\n",
+    "You can adjust this architecture to your needs and increase its size when testing the workflow:\n",
+    "- To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary.\n",
+    "- To increase the number of fully connected layers, update the mode architecture itself."
    ]
   },
   {
@@ -252,7 +276,12 @@
    "source": [
     "### Create tracking class\n",
     "\n",
-    "This section creates a `HookManager` class that allows us to capture the **activations** and **gradients** from each layer. This class accepts a PyTorch model object as an input. This class will automatically capture the gradients and activations in each layer of the model. Below, you can see a pseudo implementation:\n",
+    "This section creates a `HookManager` class:\n",
+    "- It allows to capture the **activations** and **gradients** from each layer.\n",
+    "- It accepts a PyTorch model object as an input.\n",
+    "- It automatically captures the gradients and activations in each layer of the model.\n",
+    "\n",
+    "See a pseudo implementation:\n",
     "\n",
     "```python\n",
     "# Initialize model\n",
@@ -273,7 +302,7 @@
     "    # Log values (mean, std, etc.) to Neptune\n",
     "```\n",
     "\n",
-    "_Important_: The `HookManager` class can be used in your own training script as it only accepts a model object as input."
+    "_Important_: You can use the `HookManager` class in your own training script as it only accepts a model object as input."
    ]
   },
   {
@@ -336,7 +365,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup model training\n",
+    "## Set up model training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "### Initialize Neptune run object and log hyperparameters"
    ]
   },
@@ -382,7 +417,9 @@
    "metadata": {},
    "source": [
     "### Execute model training loop\n",
-    "In this loop, we configure the `HookManager` and register the hooks. In your training loop, you will need to use the `get_` methods to retrieve the stored values for the activations and gradients after the forward and backward passes are complete."
+    "In this loop, we configure the `HookManager` and register the hooks.\n",
+    "\n",
+    "In your training loop, use the `get_` methods to retrieve the stored values for the activations and gradients after the forward and backward passes are complete."
    ]
   },
   {
@@ -391,7 +428,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create links to access active training run. Note - this is not required for the example to run, but for ease of illustration of the tutorial.\n",
+    "# Create links to access active training run. Note that this step isn't required for the example to run.\n",
     "print(\n",
     "    f\"View training charts: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/-/run/?customId={custom_run_id}&detailsTab=charts\"\n",
     ")\n",
@@ -491,16 +528,14 @@
    "metadata": {},
    "source": [
     "## What's next?\n",
-    "While the model is training, you can start using our _super-snappy_ web UI to browse your metrics and create custom analyses and visualizations.\n",
-    "\n",
-    "### What to do\n",
-    "1. Navigate to the charts tab of the active run to visualize the large number of metrics being logged in near real time.\n",
-    "2. Filter the metrics using our [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, use the following query in the search bar; `.*gradient+.*fc\\d`. This query will filter all metrics for the gradients of the fully connected layers. The more FC layers, the more charts will appear. \n",
-    "3. Export this filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard), using the _**Duplicate to**_ button. The saved dashboard will now only display these metrics during training.\n",
-    "4. Using our [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) feature, you can update the chart widget to display all fully connected layers gradients in one chart. Use the same query as above in the chart widget.\n",
-    "5. Create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics and more.\n",
-    "\n",
-    "You can see a generic example of the end result [here](https://scale.neptune.ai/o/examples/org/LLM-Pretraining/reports/9e6a2cad-77e7-42df-9d64-28f07d37e908).\n"
+    "While the model is training, you can start using the Neptune web app to browse your metrics and create custom analyses and visualizations:\n",
+    "1. To visualize the large number of metrics being logged in near real time, navigate to the **Charts** tab of the active run.\n",
+    "2. Filter the metrics using the [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, enter `.*gradient+.*fc\\d` in the search bar. This query filters all metrics for the gradients of the fully connected layers. The more FC layers, the more charts will appear.\n",
+    "3. Export the filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard). The saved dashboard will now only display these metrics during training.\n",
+    "4. Use the [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) and update the chart widget to display all fully connected layers gradients in one chart. Again, use the `.*gradient+.*fc\\d` query.\n",
+    "5. Create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics, and more.\n",
+    "\n",
+    "See also [a generic example of the training result](https://scale.neptune.ai/o/examples/org/LLM-Pretraining/reports/9e6a2cad-77e7-42df-9d64-28f07d37e908).\n"
    ]
   }
  ],

From 34c47f3ea7dd8e179b3a1a4de1780ac9c8b960d1 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 27 Mar 2025 15:24:20 +0100
Subject: [PATCH 074/125] chore: update pip installation line

---
 .../pytorch/notebooks/pytorch_text_model_debugging.ipynb        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 31167f5..dfe143a 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -97,7 +97,7 @@
    "outputs": [],
    "source": [
     "# Install dependencies\n",
-    "! pip install -q -U neptune_scale torch datasets"
+    "! pip install -qU neptune_scale torch datasets"
    ]
   },
   {

From a2a2bf1013a9c8fc944905bc0873eacdcb83f776 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 27 Mar 2025 15:31:40 +0100
Subject: [PATCH 075/125] chore: update url's to use the new run methods

---
 .../pytorch/notebooks/pytorch_text_model_debugging.ipynb | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index dfe143a..9e41665 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -407,9 +407,7 @@
     "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
     "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])\n",
     "\n",
-    "print(\n",
-    "    f\"View current experiment: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/runs/details?viewId=standard-view&runIdentificationKey={experiment_name}&type=experiment\"\n",
-    ")"
+    "print(run.get_experiment_url())"
    ]
   },
   {
@@ -428,11 +426,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create links to access active training run. Note that this step isn't required for the example to run.\n",
-    "print(\n",
-    "    f\"View training charts: https://scale.neptune.ai/{os.getenv(\"NEPTUNE_PROJECT\")}/-/run/?customId={custom_run_id}&detailsTab=charts\"\n",
-    ")\n",
-    "\n",
     "# Training setup\n",
     "debug_metrics = {}\n",
     "\n",

From 3da164e5b1accd36ab4015c8feed6a6aa277eef6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 28 Mar 2025 13:15:37 +0100
Subject: [PATCH 076/125] refactor: update example to start using an external
 class for hooking and logging layer-wise metrics

---
 .../pytorch/notebooks/TorchWatcher.py         | 239 ++++++++++++++++
 .../pytorch_text_model_debugging.ipynb        | 255 +++++++++++++++---
 2 files changed, 458 insertions(+), 36 deletions(-)
 create mode 100644 integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py

diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
new file mode 100644
index 0000000..ababcee
--- /dev/null
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -0,0 +1,239 @@
+
+import torch
+import torch.nn as nn
+from typing import Literal, List, Optional, Union, Dict, Any
+import warnings
+from contextlib import contextmanager
+
+class HookManager:
+    """
+    A robust hook management class for PyTorch models to track activations, gradients, and parameters.
+    
+    Improvements:
+    - More comprehensive error handling
+    - Flexible hook registration
+    - Support for more layer types
+    - Configurable tracking
+    """
+    def __init__(self, model: nn.Module, track_layers: Optional[List[type]] = None):
+        """
+        Initialize HookManager with additional configuration options.
+        
+        Args:
+            model (nn.Module): The PyTorch model to track
+            track_layers (Optional[List[type]]): List of layer types to track. 
+                                                 Defaults to common layer types if not specified.
+        """
+        if not isinstance(model, nn.Module):
+            raise TypeError("The model must be a PyTorch model")
+
+        self.model = model
+        self.hooks: List[torch.utils.hooks.RemovableHandle] = []
+        self.activations: Dict[str, torch.Tensor] = {}
+        self.gradients: Dict[str, torch.Tensor] = {}
+        
+        # Default layer types to track if not specified
+        self.track_layers = track_layers or [
+            nn.Linear, 
+            nn.Conv1d, 
+            nn.Conv2d, 
+            nn.Conv3d, 
+            nn.LSTM, 
+            nn.GRU, 
+            nn.RNN
+        ]
+
+    def save_activation(self, name: str):
+        """Create a forward hook to save layer activations."""
+        def hook(module, input, output):
+            try:
+                # Handle different output types (tensor or tuple)
+                activation = output[0] if isinstance(output, tuple) else output
+                self.activations[name] = activation.detach()
+            except Exception as e:
+                warnings.warn(f"Could not save activation for {name}: {e}")
+        return hook
+
+    def save_gradient(self, name: str):
+        """Create a backward hook to save layer gradients."""
+        def hook(module, grad_input, grad_output):
+            try:
+                # Save the first gradient output
+                self.gradients[name] = grad_output[0].detach()
+            except Exception as e:
+                warnings.warn(f"Could not save gradient for {name}: {e}")
+        return hook
+
+    def register_hooks(self, track_activations: bool = True, track_gradients: bool = True):
+        """
+        Register hooks for the model with configurable tracking.
+        
+        Args:
+            track_activations (bool): Whether to track layer activations
+            track_gradients (bool): Whether to track layer gradients
+        """
+        # Clear existing hooks
+        self.remove_hooks()
+
+        # Register forward hooks for activations
+        if track_activations:
+            for name, module in self.model.named_modules():
+                if any(isinstance(module, layer_type) for layer_type in self.track_layers):
+                    hook = module.register_forward_hook(self.save_activation(name))
+                    self.hooks.append(hook)
+
+        # Register backward hooks for gradients
+        if track_gradients:
+            for name, module in self.model.named_modules():
+                if any(isinstance(module, layer_type) for layer_type in self.track_layers):
+                    hook = module.register_full_backward_hook(self.save_gradient(name))
+                    self.hooks.append(hook)
+
+    def remove_hooks(self):
+        """Remove all registered hooks."""
+        for hook in self.hooks:
+            hook.remove()
+        self.hooks.clear()
+
+    def clear(self):
+        """Clear stored activations and gradients."""
+        self.activations.clear()
+        self.gradients.clear()
+
+    def get_activations(self) -> Dict[str, torch.Tensor]:
+        """Get stored activations."""
+        return self.activations
+
+    def get_gradients(self) -> Dict[str, torch.Tensor]:
+        """Get stored gradients."""
+        return self.gradients
+
+    def __del__(self):
+        """Ensure hooks are removed when the object is deleted."""
+        self.remove_hooks()
+
+
+class TorchWatcher:
+    """
+    A comprehensive tracking mechanism for PyTorch models with enhanced logging and context management.
+    """
+    def __init__(self, 
+                 model: nn.Module, 
+                 run: Any,  # Made more flexible to support different logging mechanisms
+                 track_layers: Optional[List[type]] = None
+                 ) -> None:
+        """
+        Initialize TorchWatcher with more configuration options.
+        
+        Args:
+            model (nn.Module): The PyTorch model to watch
+            run: Logging mechanism (e.g., Weights & Biases Run object)
+            track_layers (Optional[List[type]]): Layer types to specifically track
+        """
+        self.model = model
+        self.run = run
+        self.hm = HookManager(model, track_layers)
+        self.debug_metrics: Dict[str, float] = {}
+        
+        # Default hook registration
+        self.hm.register_hooks()
+
+    def _safe_tensor_stats(self, tensor: torch.Tensor) -> Dict[str, float]:
+        """
+        Safely compute tensor statistics with error handling.
+        
+        Args:
+            tensor (torch.Tensor): Input tensor
+        
+        Returns:
+            Dict of statistical metrics
+        """
+        try:
+            return {
+                'mean': tensor.mean().item(),
+                'std': tensor.std().item(),
+                'norm': tensor.norm().item(),
+                'min': tensor.min().item(),
+                'max': tensor.max().item()
+            }
+        except Exception as e:
+            warnings.warn(f"Could not compute tensor statistics: {e}")
+            return {}
+
+    def track_activations(self): 
+        """Track layer activations with enhanced statistics."""
+        activations = self.hm.get_activations()
+        for layer, activation in activations.items():
+            if activation is not None:
+                stats = self._safe_tensor_stats(activation)
+                for stat_name, stat_value in stats.items():
+                    self.debug_metrics[f"debug/activation/{layer}_{stat_name}"] = stat_value
+
+    def track_gradients(self):
+        """Track layer gradients with enhanced statistics."""
+        gradients = self.hm.get_gradients()
+        for layer, gradient in gradients.items():
+            if gradient is not None:
+                stats = self._safe_tensor_stats(gradient)
+                for stat_name, stat_value in stats.items():
+                    self.debug_metrics[f"debug/gradient/{layer}_{stat_name}"] = stat_value
+
+    def track_parameters(self):
+        """Track model parameters with enhanced statistics."""
+        for layer, param in self.model.named_parameters():
+            if param is not None and param.grad is not None:
+                stats = self._safe_tensor_stats(param.grad)
+                for stat_name, stat_value in stats.items():
+                    self.debug_metrics[f"debug/parameters/{layer}_{stat_name}"] = stat_value
+
+    def watch(self, 
+              step: Union[int, float], 
+              log: Optional[List[Literal["gradients", "parameters", "activations"]]] = None
+              ):
+        """
+        Log debug metrics with flexible configuration.
+        
+        Args:
+            step (int|float): Logging step
+            log (Optional[List]): Specific tracking modes. 
+                                  Defaults to all if None.
+        """
+        # Reset metrics
+        self.debug_metrics.clear()
+
+        # Determine tracking modes
+        if log is None or log == "all":
+            self.track_gradients()
+            self.track_parameters()
+            self.track_activations()
+        else:
+            for mode in log:
+                if mode == "gradients":
+                    self.track_gradients()
+                elif mode == "parameters":
+                    self.track_parameters()
+                elif mode == "activations":
+                    self.track_activations()
+
+        # Log metrics
+        try:
+            self.run.log_metrics(data=self.debug_metrics, step=step)
+        except Exception as e:
+            warnings.warn(f"Logging failed: {e}")
+
+        # Clear hooks
+        self.hm.clear()
+
+    @contextmanager
+    def track(self, step: Union[int, float], log: Optional[List[Literal["gradients", "parameters", "activations"]]] = None):
+        """
+        Context manager for tracking with automatic hook management.
+        
+        Args:
+            step (int|float): Logging step
+            log (Optional[List]): Specific tracking modes
+        """
+        try:
+            yield self
+        finally:
+            self.watch(step, log)
diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 9e41665..dfb5396 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -102,9 +102,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -116,7 +125,9 @@
     "from datasets import load_dataset\n",
     "\n",
     "from neptune_scale import Run\n",
-    "import os"
+    "import os\n",
+    "\n",
+    "from typing import Literal"
    ]
   },
   {
@@ -128,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,9 +173,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 81926 \n",
+      "Validation samples: 935\n"
+     ]
+    }
+   ],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
     "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
@@ -194,9 +214,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary size: 128257\n"
+     ]
+    }
+   ],
    "source": [
     "train_subset = data_subset[\"train\"].with_format(\n",
     "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
@@ -231,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +386,62 @@
     "\n",
     "    # Function to get gradients\n",
     "    def get_gradients(self):\n",
-    "        return self.gradients"
+    "        return self.gradients\n",
+    "\n",
+    "\n",
+    "from typing import Literal, List, Optional  \n",
+    "class TorchWatcher:\n",
+    "    def __init__(self, \n",
+    "                 model: nn.Module, \n",
+    "                 run: Run, \n",
+    "                 ) -> None:\n",
+    "        \n",
+    "        self.model = model\n",
+    "        self.run = run\n",
+    "        self.hm = HookManager(model)\n",
+    "        self.hm.register_hooks()\n",
+    "        self.debug_metrics = {}\n",
+    "\n",
+    "    def track_activations(self): \n",
+    "        # Track activations\n",
+    "        activations = self.hm.get_activations()\n",
+    "        for layer, activation in activations.items():\n",
+    "            if layer is not None:\n",
+    "                self.debug_metrics[f\"debug/activation/{layer}_mean\"] = activation[0].mean().item()\n",
+    "                self.debug_metrics[f\"debug/activation/{layer}_std\"] = activation[0].std().item()\n",
+    "\n",
+    "    def track_gradients(self):\n",
+    "        # Track gradients with hooks\n",
+    "        gradients = self.hm.get_gradients()\n",
+    "        for layer, gradient in gradients.items():\n",
+    "            self.debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
+    "\n",
+    "    def track_parameters(self):\n",
+    "        # Track gradients per layer at each epoch\n",
+    "        for layer, param in self.model.named_parameters():\n",
+    "            if param is not None:\n",
+    "                self.debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
+    "                self.debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
+    "                self.debug_metrics[f\"debug/parameters/{layer}_norm\"] = (\n",
+    "                    param.grad.norm().item()\n",
+    "                )  # L2 norm (Euclidean norm) of the gradients\n",
+    "\n",
+    "    def watch(self, step: int|float, log: Optional[List[Literal[\"gradients\", \"parameters\", \"activations\"]]] | None = \"all\"):\n",
+    "        match log:\n",
+    "            case \"gradients\":\n",
+    "                self.track_gradients()\n",
+    "            case \"parameters\":\n",
+    "                self.track_parameters()\n",
+    "            case \"all\":\n",
+    "                self.track_gradients()\n",
+    "                self.track_parameters()\n",
+    "\n",
+    "        self.run.log_metrics(\n",
+    "            data=self.debug_metrics,\n",
+    "            step=step\n",
+    "        )\n",
+    "\n",
+    "        self.hm.clear()"
    ]
   },
   {
@@ -377,9 +460,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment\n"
+     ]
+    }
+   ],
    "source": [
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
@@ -424,7 +515,118 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 1 / 10241, Loss: 11.762494087219238\n",
+      "1\n",
+      "gradients\n",
+      "Step 2 / 10241, Loss: 11.673644065856934\n",
+      "2\n",
+      "gradients\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-27 18:14:46,982 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n",
+      "2025-03-27 18:14:46,987 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 3 / 10241, Loss: 10.27919864654541\n",
+      "3\n",
+      "gradients\n",
+      "Step 4 / 10241, Loss: 9.545785903930664\n",
+      "4\n",
+      "gradients\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-27 18:14:52,233 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n",
+      "2025-03-27 18:14:52,234 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 5 / 10241, Loss: 9.699625968933105\n",
+      "5\n",
+      "gradients\n",
+      "Step 6 / 10241, Loss: 9.690692901611328\n",
+      "6\n",
+      "gradients\n",
+      "Step 7 / 10241, Loss: 10.060093879699707\n",
+      "7\n",
+      "gradients\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[19], line 36\u001b[0m\n\u001b[0;32m     33\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m     35\u001b[0m \u001b[38;5;66;03m# Forward pass\u001b[39;00m\n\u001b[1;32m---> 36\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     38\u001b[0m \u001b[38;5;66;03m# Compute the loss (ignore padding tokens by masking labels)\u001b[39;00m\n\u001b[0;32m     39\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[5], line 12\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     10\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[0;32m     11\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm(x)  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfc1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlstm_out\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "# Training setup\n",
     "debug_metrics = {}\n",
@@ -438,8 +640,8 @@
     "    ignore_index=-100\n",
     ")  # Ignore the buffering index of -100 in the dataset\n",
     "\n",
-    "hook_manager = HookManager(model)\n",
-    "hook_manager.register_hooks()\n",
+    "# Define watcher class\n",
+    "watcher = TorchWatcher(model=model, run=run)\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model.to(device)\n",
@@ -451,7 +653,6 @@
     "    for batch in train_dataloader:\n",
     "        model.train()\n",
     "        step_counter += 1\n",
-    "        hook_manager.clear()\n",
     "\n",
     "        input_ids = batch[\"input_ids\"].to(device)\n",
     "        labels = batch[\"labels\"].to(device)\n",
@@ -471,26 +672,8 @@
     "        total_loss += loss.item()\n",
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
     "\n",
-    "        # Track activations\n",
-    "        activations = hook_manager.get_activations()\n",
-    "        for layer, activation in activations.items():\n",
-    "            if layer is not None:\n",
-    "                debug_metrics[f\"debug/activation/{layer}_mean\"] = activation[0].mean().item()\n",
-    "                debug_metrics[f\"debug/activation/{layer}_std\"] = activation[0].std().item()\n",
-    "\n",
-    "        # Track gradients with hooks\n",
-    "        gradients = hook_manager.get_gradients()\n",
-    "        for layer, gradient in gradients.items():\n",
-    "            debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
-    "\n",
-    "        # Track gradients per layer at each epoch\n",
-    "        for layer, param in model.named_parameters():\n",
-    "            if param is not None:\n",
-    "                debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
-    "                debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
-    "                debug_metrics[f\"debug/parameters/{layer}_norm\"] = (\n",
-    "                    param.grad.norm().item()\n",
-    "                )  # L2 norm (Euclidean norm) of the gradients\n",
+    "        # Call watch() method in loop determing which layer-wise metrics to watch\n",
+    "        watcher.watch(step=step_counter, log=\"gradients\")\n",
     "\n",
     "        if step_counter % 50 == 0:  # Log validation loss at every 50 steps\n",
     "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",

From f44323d5e9232be466902c73db636ed54aa7dc9f Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 31 Mar 2025 16:27:58 +0200
Subject: [PATCH 077/125] feat: create a TorchWatcher package

- package to initialize hooks for Pytorch models, replacing theHookManager class
- add readme.md for using the package
- update the degbugging pytorch example to use the new package
---
 .../pytorch/notebooks/README.md               | 115 +++++++
 .../pytorch/notebooks/TorchWatcher.py         | 136 ++++----
 .../pytorch_text_model_debugging.ipynb        | 320 ++----------------
 3 files changed, 230 insertions(+), 341 deletions(-)
 create mode 100644 integrations-and-supported-tools/pytorch/notebooks/README.md

diff --git a/integrations-and-supported-tools/pytorch/notebooks/README.md b/integrations-and-supported-tools/pytorch/notebooks/README.md
new file mode 100644
index 0000000..07e74b7
--- /dev/null
+++ b/integrations-and-supported-tools/pytorch/notebooks/README.md
@@ -0,0 +1,115 @@
+# TorchWatcher
+
+TorchWatcher is a powerful tool for monitoring PyTorch models during training. It helps you track activations, gradients, and parameters of your model layers in real-time using neptune.ai for logging.
+
+## Features
+
+- Track layer activations, gradients, and parameters
+- Automatic hook management
+- Select from predefined tensor statistics
+- Flexible tracking configuration
+- Support for various layer types (Linear, Conv1d/2d/3d, LSTM, GRU, RNN)
+
+## Installation
+
+Make sure you have the required dependencies:
+```bash
+pip install torch neptune_scale
+```
+
+## Quick Start
+
+1. Initialize your Neptune run:
+```python
+from neptune_scale import Run
+run = Run(
+    experiment_name="your-experiment-name",
+)
+```
+
+2. Create your PyTorch model and initialize TorchWatcher:
+```python
+from TorchWatcher import TorchWatcher
+
+model = YourModel()
+watcher = TorchWatcher(model, run)  # Uses default mean() statistic
+```
+
+3. Use the watcher in your training loop:
+```python
+# Forward pass
+output = model(x_batch)
+loss = criterion(output, y_batch)
+
+# Backward pass
+optimizer.zero_grad()
+loss.backward()
+optimizer.step()
+
+# Track metrics after the forward and backward passes
+watcher.watch(step=step)
+```
+
+## Advanced Usage
+
+### Tensor Statistics
+
+You can select which predefined statistics to compute:
+```python
+watcher = TorchWatcher(
+    model, 
+    run,
+    tensor_stats=['mean', 'std', 'norm']  # Select from available statistics
+)
+```
+
+Available statistics:
+- `mean`: Mean value
+- `std`: Standard deviation
+- `norm`: L2 norm
+- `min`: Minimum value
+- `max`: Maximum value
+- `var`: Variance
+- `abs_mean`: Mean of absolute values
+
+By default, only the mean statistic is computed if no statistics are specified.
+
+### Custom Layer Tracking
+
+You can specify which layer types to track:
+```python
+watcher = TorchWatcher(
+    model, 
+    run,
+    track_layers=[nn.Linear, nn.Conv2d]  # Only track Linear and Conv2d layers
+)
+```
+
+### Selective Tracking
+
+You can choose which metrics to track:
+```python
+watcher.watch(
+    step=epoch,
+    log=["gradients", "parameters"]  # Only track gradients and parameters, not activations
+)
+```
+
+## Example
+
+See `torch_watcher_example.py` for a complete working example that demonstrates:
+- Model definition
+- Data generation
+- Training loop with metrics tracking
+- Validation
+- Neptune integration
+
+## Logged Metrics
+
+The following metrics are automatically logged to Neptune:
+
+- `debug/activation/{layer}_{stat_name}`: Layer activation statistics
+- `debug/gradient/{layer}_{stat_name}`: Layer gradient statistics
+- `debug/parameters/{layer}_{stat_name}`: Parameter statistics
+
+Where `stat_name` corresponds to the statistics you selected. By default, only the mean statistic is computed. 
\ No newline at end of file
diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index ababcee..7d9dd08 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -1,27 +1,39 @@
+import warnings
+from typing import Any, Dict, List, Literal, Optional, Union
 
 import torch
 import torch.nn as nn
-from typing import Literal, List, Optional, Union, Dict, Any
-import warnings
-from contextlib import contextmanager
+
+# Predefined tensor statistics
+TENSOR_STATS = {
+    "mean": lambda x: x.mean().item(),
+    "std": lambda x: x.std().item(),
+    "norm": lambda x: x.norm().item(),
+    "min": lambda x: x.min().item(),
+    "max": lambda x: x.max().item(),
+    "var": lambda x: x.var().item(),
+    "abs_mean": lambda x: x.abs().mean().item(),
+}
+
 
 class HookManager:
     """
     A robust hook management class for PyTorch models to track activations, gradients, and parameters.
-    
+
     Improvements:
     - More comprehensive error handling
     - Flexible hook registration
     - Support for more layer types
     - Configurable tracking
     """
+
     def __init__(self, model: nn.Module, track_layers: Optional[List[type]] = None):
         """
         Initialize HookManager with additional configuration options.
-        
+
         Args:
             model (nn.Module): The PyTorch model to track
-            track_layers (Optional[List[type]]): List of layer types to track. 
+            track_layers (Optional[List[type]]): List of layer types to track.
                                                  Defaults to common layer types if not specified.
         """
         if not isinstance(model, nn.Module):
@@ -31,20 +43,21 @@ def __init__(self, model: nn.Module, track_layers: Optional[List[type]] = None):
         self.hooks: List[torch.utils.hooks.RemovableHandle] = []
         self.activations: Dict[str, torch.Tensor] = {}
         self.gradients: Dict[str, torch.Tensor] = {}
-        
+
         # Default layer types to track if not specified
         self.track_layers = track_layers or [
-            nn.Linear, 
-            nn.Conv1d, 
-            nn.Conv2d, 
-            nn.Conv3d, 
-            nn.LSTM, 
-            nn.GRU, 
-            nn.RNN
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nn.LSTM,
+            nn.GRU,
+            nn.RNN,
         ]
 
     def save_activation(self, name: str):
         """Create a forward hook to save layer activations."""
+
         def hook(module, input, output):
             try:
                 # Handle different output types (tensor or tuple)
@@ -52,22 +65,25 @@ def hook(module, input, output):
                 self.activations[name] = activation.detach()
             except Exception as e:
                 warnings.warn(f"Could not save activation for {name}: {e}")
+
         return hook
 
     def save_gradient(self, name: str):
         """Create a backward hook to save layer gradients."""
+
         def hook(module, grad_input, grad_output):
             try:
                 # Save the first gradient output
                 self.gradients[name] = grad_output[0].detach()
             except Exception as e:
                 warnings.warn(f"Could not save gradient for {name}: {e}")
+
         return hook
 
     def register_hooks(self, track_activations: bool = True, track_gradients: bool = True):
         """
         Register hooks for the model with configurable tracking.
-        
+
         Args:
             track_activations (bool): Whether to track layer activations
             track_gradients (bool): Whether to track layer gradients
@@ -117,50 +133,69 @@ class TorchWatcher:
     """
     A comprehensive tracking mechanism for PyTorch models with enhanced logging and context management.
     """
-    def __init__(self, 
-                 model: nn.Module, 
-                 run: Any,  # Made more flexible to support different logging mechanisms
-                 track_layers: Optional[List[type]] = None
-                 ) -> None:
+
+    def __init__(
+        self,
+        model: nn.Module,
+        run: Any,  # Made more flexible to support different logging mechanisms
+        track_layers: Optional[List[type]] = None,
+        tensor_stats: Optional[List[str]] = None,
+    ) -> None:
         """
         Initialize TorchWatcher with more configuration options.
-        
+
         Args:
             model (nn.Module): The PyTorch model to watch
-            run: Logging mechanism (e.g., Weights & Biases Run object)
+            run: Logging mechanism from Neptune
             track_layers (Optional[List[type]]): Layer types to specifically track
+            tensor_stats (Optional[List[str]]): List of statistics to compute.
+                                              Available options: mean, std, norm, min, max, var, abs_mean.
+                                              Defaults to ['mean'] if not specified.
         """
+        if not isinstance(model, nn.Module):
+            raise TypeError("The model must be a PyTorch model")
+
         self.model = model
         self.run = run
         self.hm = HookManager(model, track_layers)
         self.debug_metrics: Dict[str, float] = {}
-        
+
+        # Validate and set tensor statistics
+        if tensor_stats is None:
+            tensor_stats = ["mean"]
+
+        # Validate that all requested statistics exist
+        invalid_stats = [stat for stat in tensor_stats if stat not in TENSOR_STATS]
+        if invalid_stats:
+            raise ValueError(
+                f"Invalid statistics requested: {invalid_stats}. "
+                f"Available statistics are: {list(TENSOR_STATS.keys())}"
+            )
+
+        self.tensor_stats = {stat: TENSOR_STATS[stat] for stat in tensor_stats}
+
         # Default hook registration
         self.hm.register_hooks()
 
     def _safe_tensor_stats(self, tensor: torch.Tensor) -> Dict[str, float]:
         """
         Safely compute tensor statistics with error handling.
-        
+
         Args:
             tensor (torch.Tensor): Input tensor
-        
+
         Returns:
             Dict of statistical metrics
         """
-        try:
-            return {
-                'mean': tensor.mean().item(),
-                'std': tensor.std().item(),
-                'norm': tensor.norm().item(),
-                'min': tensor.min().item(),
-                'max': tensor.max().item()
-            }
-        except Exception as e:
-            warnings.warn(f"Could not compute tensor statistics: {e}")
-            return {}
+        stats = {}
+        for stat_name, stat_func in self.tensor_stats.items():
+            try:
+                stats[stat_name] = stat_func(tensor)
+            except Exception as e:
+                warnings.warn(f"Could not compute {stat_name} statistic: {e}")
+        return stats
 
-    def track_activations(self): 
+    def track_activations(self):
         """Track layer activations with enhanced statistics."""
         activations = self.hm.get_activations()
         for layer, activation in activations.items():
@@ -186,16 +221,17 @@ def track_parameters(self):
                 for stat_name, stat_value in stats.items():
                     self.debug_metrics[f"debug/parameters/{layer}_{stat_name}"] = stat_value
 
-    def watch(self, 
-              step: Union[int, float], 
-              log: Optional[List[Literal["gradients", "parameters", "activations"]]] = None
-              ):
+    def watch(
+        self,
+        step: Union[int, float],
+        log: Optional[List[Literal["gradients", "parameters", "activations"]]] = None,
+    ):
         """
         Log debug metrics with flexible configuration.
-        
+
         Args:
             step (int|float): Logging step
-            log (Optional[List]): Specific tracking modes. 
+            log (Optional[List]): Specific tracking modes.
                                   Defaults to all if None.
         """
         # Reset metrics
@@ -223,17 +259,3 @@ def watch(self,
 
         # Clear hooks
         self.hm.clear()
-
-    @contextmanager
-    def track(self, step: Union[int, float], log: Optional[List[Literal["gradients", "parameters", "activations"]]] = None):
-        """
-        Context manager for tracking with automatic hook management.
-        
-        Args:
-            step (int|float): Logging step
-            log (Optional[List]): Specific tracking modes
-        """
-        try:
-            yield self
-        finally:
-            self.watch(step, log)
diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index dfb5396..1d7dadc 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -102,18 +102,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -139,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,18 +164,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 81926 \n",
-      "Validation samples: 935\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
     "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
@@ -214,17 +196,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vocabulary size: 128257\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_subset = data_subset[\"train\"].with_format(\n",
     "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
@@ -259,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,35 +276,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create tracking class\n",
+    "### Setup tracking class\n",
     "\n",
-    "This section creates a `HookManager` class:\n",
-    "- It allows to capture the **activations** and **gradients** from each layer.\n",
-    "- It accepts a PyTorch model object as an input.\n",
-    "- It automatically captures the gradients and activations in each layer of the model.\n",
+    "This section intializes the `TorchWatcher` class:\n",
+    "- It accepts a PyTorch model object as an input. \n",
+    "- It allows you to capture the **parameters**, **activations** and **gradients** from each layer.\n",
+    "- Specify which tensor statistics to capture.\n",
     "\n",
     "See a pseudo implementation:\n",
     "\n",
     "```python\n",
-    "# Initialize model\n",
-    "model = your_ModelClass()\n",
-    "# Register hooks\n",
-    "hm = HookManager(model)\n",
-    "hm.register_hooks()\n",
+    "from TorchWatcher import TorchWatcher\n",
+    "\n",
+    "model = YourModel()\n",
+    "watcher = TorchWatcher(model, run)  # Uses default mean() statistic\n",
     "\n",
     "# Training loop\n",
     "for epoch in range(3):\n",
-    "    \n",
-    "    # Forward pass, e.g. model.train()\n",
-    "    # Backward pass, e.g. loss.backward()\n",
-    "    \n",
-    "    activations = hm.get_activations()\n",
-    "    gradients = hm.get_gradients()\n",
-    "\n",
-    "    # Log values (mean, std, etc.) to Neptune\n",
-    "```\n",
-    "\n",
-    "_Important_: You can use the `HookManager` class in your own training script as it only accepts a model object as input."
+    "    # Forward pass\n",
+    "    output = model(x_batch)\n",
+    "    loss = criterion(output, y_batch)\n",
+    "\n",
+    "    # Backward pass\n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    # Track metrics after the forward and backward passes\n",
+    "    watcher.watch(step=epoch)\n",
+    "```"
    ]
   },
   {
@@ -339,109 +313,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# A class to manage hooks for activations and gradients\n",
-    "class HookManager:\n",
-    "    def __init__(self, model):\n",
-    "        if not isinstance(model, nn.Module):\n",
-    "            raise TypeError(\"The model must be a PyTorch model\")\n",
-    "\n",
-    "        self.model = model\n",
-    "        self.hooks = []\n",
-    "        self.activations = {}\n",
-    "        self.gradients = {}\n",
-    "\n",
-    "    # Function to save activations\n",
-    "    def save_activation(self, name):\n",
-    "        def hook(module, input, output):\n",
-    "            self.activations[name] = output\n",
-    "\n",
-    "        return hook\n",
-    "\n",
-    "    # Function to save gradients (registering hooks for the model parameters)\n",
-    "    def save_gradient(self, name):\n",
-    "        def hook(module, grad_input, grad_output):\n",
-    "            self.gradients[name] = grad_output[0]\n",
-    "\n",
-    "        return hook\n",
-    "\n",
-    "    # Function to register hooks for activations and gradients\n",
-    "    def register_hooks(self):\n",
-    "        # Register forward hooks for activations\n",
-    "        for name, module in self.model.named_modules():\n",
-    "            self.hooks.append(module.register_forward_hook(self.save_activation(name)))\n",
-    "\n",
-    "        # Register backward hooks for gradients\n",
-    "        for name, module in self.model.named_modules():\n",
-    "            if isinstance(module, (nn.LSTM, nn.Linear)):  # You can add more layer types here\n",
-    "                self.hooks.append(module.register_full_backward_hook(self.save_gradient(name)))\n",
-    "\n",
-    "    # Function to clear activations and gradients after use\n",
-    "    def clear(self):\n",
-    "        self.activations = {}\n",
-    "        self.gradients = {}\n",
-    "\n",
-    "    # Function to get activations\n",
-    "    def get_activations(self):\n",
-    "        return self.activations\n",
-    "\n",
-    "    # Function to get gradients\n",
-    "    def get_gradients(self):\n",
-    "        return self.gradients\n",
-    "\n",
-    "\n",
-    "from typing import Literal, List, Optional  \n",
-    "class TorchWatcher:\n",
-    "    def __init__(self, \n",
-    "                 model: nn.Module, \n",
-    "                 run: Run, \n",
-    "                 ) -> None:\n",
-    "        \n",
-    "        self.model = model\n",
-    "        self.run = run\n",
-    "        self.hm = HookManager(model)\n",
-    "        self.hm.register_hooks()\n",
-    "        self.debug_metrics = {}\n",
-    "\n",
-    "    def track_activations(self): \n",
-    "        # Track activations\n",
-    "        activations = self.hm.get_activations()\n",
-    "        for layer, activation in activations.items():\n",
-    "            if layer is not None:\n",
-    "                self.debug_metrics[f\"debug/activation/{layer}_mean\"] = activation[0].mean().item()\n",
-    "                self.debug_metrics[f\"debug/activation/{layer}_std\"] = activation[0].std().item()\n",
-    "\n",
-    "    def track_gradients(self):\n",
-    "        # Track gradients with hooks\n",
-    "        gradients = self.hm.get_gradients()\n",
-    "        for layer, gradient in gradients.items():\n",
-    "            self.debug_metrics[f\"debug/gradient/{layer}_mean\"] = gradient.mean().item()\n",
-    "\n",
-    "    def track_parameters(self):\n",
-    "        # Track gradients per layer at each epoch\n",
-    "        for layer, param in self.model.named_parameters():\n",
-    "            if param is not None:\n",
-    "                self.debug_metrics[f\"debug/parameters/{layer}_std\"] = param.grad.std().item()\n",
-    "                self.debug_metrics[f\"debug/parameters/{layer}_mean\"] = param.grad.mean().item()\n",
-    "                self.debug_metrics[f\"debug/parameters/{layer}_norm\"] = (\n",
-    "                    param.grad.norm().item()\n",
-    "                )  # L2 norm (Euclidean norm) of the gradients\n",
-    "\n",
-    "    def watch(self, step: int|float, log: Optional[List[Literal[\"gradients\", \"parameters\", \"activations\"]]] | None = \"all\"):\n",
-    "        match log:\n",
-    "            case \"gradients\":\n",
-    "                self.track_gradients()\n",
-    "            case \"parameters\":\n",
-    "                self.track_parameters()\n",
-    "            case \"all\":\n",
-    "                self.track_gradients()\n",
-    "                self.track_parameters()\n",
-    "\n",
-    "        self.run.log_metrics(\n",
-    "            data=self.debug_metrics,\n",
-    "            step=step\n",
-    "        )\n",
-    "\n",
-    "        self.hm.clear()"
+    "from TorchWatcher import TorchWatcher"
    ]
   },
   {
@@ -460,17 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
@@ -506,127 +370,15 @@
    "metadata": {},
    "source": [
     "### Execute model training loop\n",
-    "In this loop, we configure the `HookManager` and register the hooks.\n",
     "\n",
-    "In your training loop, use the `get_` methods to retrieve the stored values for the activations and gradients after the forward and backward passes are complete."
+    "In the training loop we call the `watch()` method from the `TorchWatcher` package after the backward and forward passes to track our parameters, gradients and activations with a combination of tensor statistics. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 1 / 10241, Loss: 11.762494087219238\n",
-      "1\n",
-      "gradients\n",
-      "Step 2 / 10241, Loss: 11.673644065856934\n",
-      "2\n",
-      "gradients\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-03-27 18:14:46,982 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n",
-      "2025-03-27 18:14:46,987 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 3 / 10241, Loss: 10.27919864654541\n",
-      "3\n",
-      "gradients\n",
-      "Step 4 / 10241, Loss: 9.545785903930664\n",
-      "4\n",
-      "gradients\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-03-27 18:14:52,233 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n",
-      "2025-03-27 18:14:52,234 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 5 / 10241, Loss: 9.699625968933105\n",
-      "5\n",
-      "gradients\n",
-      "Step 6 / 10241, Loss: 9.690692901611328\n",
-      "6\n",
-      "gradients\n",
-      "Step 7 / 10241, Loss: 10.060093879699707\n",
-      "7\n",
-      "gradients\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[19], line 36\u001b[0m\n\u001b[0;32m     33\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m     35\u001b[0m \u001b[38;5;66;03m# Forward pass\u001b[39;00m\n\u001b[1;32m---> 36\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     38\u001b[0m \u001b[38;5;66;03m# Compute the loss (ignore padding tokens by masking labels)\u001b[39;00m\n\u001b[0;32m     39\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[5], line 12\u001b[0m, in \u001b[0;36mSimpleLLM.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     10\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding(x)\n\u001b[0;32m     11\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm(x)  \u001b[38;5;66;03m# LSTM returns output and hidden/cell state tuple\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfc1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlstm_out\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Use the last output from the LSTM\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1845\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m inner()\n\u001b[0;32m   1844\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1845\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1847\u001b[0m     \u001b[38;5;66;03m# run always called hooks if they have not already been run\u001b[39;00m\n\u001b[0;32m   1848\u001b[0m     \u001b[38;5;66;03m# For now only forward hooks have the always_call option but perhaps\u001b[39;00m\n\u001b[0;32m   1849\u001b[0m     \u001b[38;5;66;03m# this functionality should be added to full backward hooks as well.\u001b[39;00m\n\u001b[0;32m   1850\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m _global_forward_hooks\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1793\u001b[0m, in \u001b[0;36mModule._call_impl.<locals>.inner\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1790\u001b[0m     bw_hook \u001b[38;5;241m=\u001b[39m BackwardHook(\u001b[38;5;28mself\u001b[39m, full_backward_hooks, backward_pre_hooks)\n\u001b[0;32m   1791\u001b[0m     args \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(args)\n\u001b[1;32m-> 1793\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n\u001b[0;32m   1795\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook_id, hook \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[0;32m   1796\u001b[0m         \u001b[38;5;241m*\u001b[39m_global_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks\u001b[38;5;241m.\u001b[39mitems(),\n\u001b[0;32m   1798\u001b[0m     ):\n\u001b[0;32m   1799\u001b[0m         \u001b[38;5;66;03m# mark that always called hook is run\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Training setup\n",
     "debug_metrics = {}\n",
@@ -641,7 +393,7 @@
     ")  # Ignore the buffering index of -100 in the dataset\n",
     "\n",
     "# Define watcher class\n",
-    "watcher = TorchWatcher(model=model, run=run)\n",
+    "watcher = TorchWatcher(model=model, run=run, tensor_stats=[\"mean\", \"norm\"])\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model.to(device)\n",
@@ -673,7 +425,7 @@
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
     "\n",
     "        # Call watch() method in loop determing which layer-wise metrics to watch\n",
-    "        watcher.watch(step=step_counter, log=\"gradients\")\n",
+    "        watcher.watch(step=step_counter)\n",
     "\n",
     "        if step_counter % 50 == 0:  # Log validation loss at every 50 steps\n",
     "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",

From 2b001af11a9db7fc321a2394921b1ca60b39a6dd Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 31 Mar 2025 16:29:43 +0200
Subject: [PATCH 078/125] style: update intro

---
 .../pytorch/notebooks/pytorch_text_model_debugging.ipynb    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 1d7dadc..073217c 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -30,14 +30,14 @@
    "source": [
     "This guide will show you how to:\n",
     "- Initialize the **Neptune Run** object and log configuration parameters\n",
-    "- Create a **reusable class** to hook layer-wise metrics (`HookManager`)\n",
+    "- Create a **reusable class** to hook and log layer-wise metrics (`TorchWatcher`)\n",
     "- Log **aggregated metrics** such as loss and accuracy\n",
     "- Log **layer-wise metrics** to debug model training such as:\n",
     "\n",
     "| **Metric**                        | **Demonstrated** | **What it shows**                                                                                             | **How to capture**                                             |\n",
     "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
-    "| **Activations**                   | Yes                                  | Dead or exploding activations can indicate issues with training stability. | `HookManager`            |\n",
-    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | `HookManager`        |\n",
+    "| **Activations**                   | Yes                                  | Dead or exploding activations can indicate issues with training stability. | `TorchWatcher`            |\n",
+    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | `TorchWatcher`        |\n",
     "| **Parameters**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
     "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
     "| **Learning rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",

From fdfca8099158ad3e8175fad2204520ac2c7acd01 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 31 Mar 2025 18:57:15 +0200
Subject: [PATCH 079/125] update TocrhWatcher package to default to tracking
 all available layers as well as allowing a user to specify which layers to
 track

---
 .../pytorch/notebooks/README.md               | 235 +++++++++++++-----
 .../pytorch/notebooks/TorchWatcher.py         | 121 +++++++--
 .../notebooks/torch_watcher_example.py        | 120 +++++++++
 3 files changed, 388 insertions(+), 88 deletions(-)
 create mode 100644 integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py

diff --git a/integrations-and-supported-tools/pytorch/notebooks/README.md b/integrations-and-supported-tools/pytorch/notebooks/README.md
index 07e74b7..90c2872 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/README.md
+++ b/integrations-and-supported-tools/pytorch/notebooks/README.md
@@ -1,115 +1,216 @@
 # TorchWatcher
 
-TorchWatcher is a powerful tool for monitoring PyTorch models during training. It helps you track activations, gradients, and parameters of your model layers in real-time using neptune.ai for logging.
+A simple PyTorch hook manager for tracking model metrics during training.
 
 ## Features
 
-- Track layer activations, gradients, and parameters
-- Automatic hook management
-- Select from predefined tensor statistics
-- Flexible tracking configuration
-- Support for various layer types (Linear, Conv1d/2d/3d, LSTM, GRU, RNN)
+- Track predefined statistics for specified PyTorch layers
+- Automatic metric logging to Neptune
+- Support for both training and validation phases
+- Selective layer tracking
+- Predefined statistics options:
+  - `mean`: Mean value
+  - `std`: Standard deviation
+  - `norm`: L2 norm
+  - `min`: Minimum value
+  - `max`: Maximum value
+  - `var`: Variance
+  - `abs_mean`: Mean of absolute values
 
 ## Installation
 
-Make sure you have the required dependencies:
 ```bash
-pip install torch neptune_scale
+pip install neptune-client
 ```
 
-## Quick Start
+## Usage
+
+### Basic Usage
 
-1. Initialize your Neptune run:
 ```python
 from neptune_scale import Run
+from TorchWatcher import TorchWatcher
+
+# Initialize Neptune run
 run = Run(
-    experiment_name="your-experiment-name",
+    experiment_name="my-experiment",
 )
-```
-
-2. Create your PyTorch model and initialize TorchWatcher:
-```python
-from TorchWatcher import TorchWatcher
 
+# Create your model
 model = YourModel()
-watcher = TorchWatcher(model, run)  # Uses default mean() statistic
-```
 
-3. Use the watcher in your training loop:
-```python
-# Forward pass
-output = model(x_batch)
-loss = criterion(output, y_batch)
+# Option 1: Track all layers (default)
+watcher = TorchWatcher(
+    model,
+    run,
+    tensor_stats=['mean', 'norm']   # Optional: select from predefined statistics
+)
+
+# Option 2: Track specific layer types
+layers_to_track = [
+    nn.Linear,    # Track all linear layers
+    nn.Conv2d,    # Track all convolutional layers
+    nn.ReLU,      # Track all ReLU layers
+]
 
-# Backward pass
-optimizer.zero_grad()
-loss.backward()
-optimizer.step()
+watcher = TorchWatcher(
+    model,
+    run,
+    track_layers=layers_to_track,  # Specify which layers to track
+    tensor_stats=['mean', 'norm']   # Optional: select from predefined statistics
+)
 
-# Track metrics after the forward and backward passes
-watcher.watch(step=step)
+# Training loop
+for epoch in range(n_epochs):
+    for batch in dataloader:
+        # Forward pass
+        output = model(batch)
+        
+        # Backward pass
+        loss.backward()
+        
+        # Track metrics
+        watcher.watch(step=current_step)
 ```
 
-## Advanced Usage
+### Detailed Examples
+
+#### 1. Using Default Settings
 
-### Tensor Statistics
+The simplest way to use TorchWatcher is with default settings, which will track all layers in your model:
 
-You can select which predefined statistics to compute:
 ```python
+# Initialize with defaults
 watcher = TorchWatcher(
-    model, 
-    run,
-    tensor_stats=['mean', 'std', 'norm']  # Select from available statistics
+    model,
+    run
 )
-```
 
-Available statistics:
-- `mean`: Mean value
-- `std`: Standard deviation
-- `norm`: L2 norm
-- `min`: Minimum value
-- `max`: Maximum value
-- `var`: Variance
-- `abs_mean`: Mean of absolute values
+# This will:
+# - Track all layers in the model
+# - Use only the 'mean' statistic (default)
+# - Log all metrics (activations, gradients, parameters)
+```
 
-By default, only the mean statistic is computed if no statistics are specified.
+#### 2. Specifying Which Layers to Track
 
-### Custom Layer Tracking
+You can choose to track specific layer types in your model:
 
-You can specify which layer types to track:
 ```python
+# Track only convolutional and linear layers
 watcher = TorchWatcher(
-    model, 
+    model,
     run,
-    track_layers=[nn.Linear, nn.Conv2d]  # Only track Linear and Conv2d layers
+    track_layers=[nn.Conv2d, nn.Linear]
+)
+
+# Track only normalization layers
+watcher = TorchWatcher(
+    model,
+    run,
+    track_layers=[nn.BatchNorm2d, nn.LayerNorm]
+)
+
+# Track only activation layers
+watcher = TorchWatcher(
+    model,
+    run,
+    track_layers=[nn.ReLU, nn.LeakyReLU, nn.GELU]
 )
 ```
 
-### Selective Tracking
+#### 3. Selecting Statistics to Capture
+
+You can specify which statistics to compute for each layer:
 
-You can choose which metrics to track:
 ```python
-watcher.watch(
-    step=epoch,
-    log=["gradients", "parameters"]  # Only track gradients and parameters, not activations
+# Track multiple statistics
+watcher = TorchWatcher(
+    model,
+    run,
+    tensor_stats=['mean', 'std', 'norm', 'min', 'max']
+)
+
+# Track only basic statistics
+watcher = TorchWatcher(
+    model,
+    run,
+    tensor_stats=['mean', 'std']
+)
+
+# Track only norm-based statistics
+watcher = TorchWatcher(
+    model,
+    run,
+    tensor_stats=['norm', 'abs_mean']
 )
 ```
 
-## Example
+#### 4. Controlling Metric Logging
+
+The `watch()` method allows you to specify which metrics to log:
 
-See `torch_watcher_example.py` for a complete working example that demonstrates:
-- Model definition
-- Data generation
-- Training loop with metrics tracking
-- Validation
-- Neptune integration
+```python
+# Log all metrics (default)
+watcher.watch(step=current_step)
 
-## Logged Metrics
+# Log only activation metrics
+watcher.watch(step=current_step, log=["activations"])
 
-The following metrics are automatically logged to Neptune:
+# Log only gradient metrics
+watcher.watch(step=current_step, log=["gradients"])
 
-- `debug/activation/{layer}_{stat_name}`: Layer activation statistics
-- `debug/gradient/{layer}_{stat_name}`: Layer gradient statistics
-- `debug/parameters/{layer}_{stat_name}`: Parameter statistics
+# Log only parameter metrics
+watcher.watch(step=current_step, log=["parameters"])
+
+# Log specific combinations
+watcher.watch(step=current_step, log=["activations", "gradients"])
+```
 
-Where `stat_name` corresponds to the statistics you selected. By default, only the mean statistic is computed. 
\ No newline at end of file
+### Layer Types
+
+You can track any of these PyTorch layer types:
+- `nn.Linear`
+- `nn.Conv1d`, `nn.Conv2d`, `nn.Conv3d`
+- `nn.ConvTranspose1d`, `nn.ConvTranspose2d`, `nn.ConvTranspose3d`
+- `nn.BatchNorm1d`, `nn.BatchNorm2d`, `nn.BatchNorm3d`
+- `nn.LayerNorm`
+- `nn.InstanceNorm1d`, `nn.InstanceNorm2d`, `nn.InstanceNorm3d`
+- `nn.GroupNorm`
+- `nn.ReLU`, `nn.LeakyReLU`, `nn.PReLU`, `nn.RReLU`, `nn.ELU`, `nn.SELU`, `nn.CELU`, `nn.GELU`
+- `nn.Sigmoid`
+- `nn.Tanh`
+- `nn.Dropout`, `nn.Dropout2d`, `nn.Dropout3d`
+- `nn.MaxPool1d`, `nn.MaxPool2d`, `nn.MaxPool3d`
+- `nn.AvgPool1d`, `nn.AvgPool2d`, `nn.AvgPool3d`
+- `nn.AdaptiveMaxPool1d`, `nn.AdaptiveMaxPool2d`, `nn.AdaptiveMaxPool3d`
+- `nn.AdaptiveAvgPool1d`, `nn.AdaptiveAvgPool2d`, `nn.AdaptiveAvgPool3d`
+- `nn.LSTM`, `nn.GRU`, `nn.RNN`
+- `nn.Embedding`
+- `nn.TransformerEncoderLayer`, `nn.TransformerDecoderLayer`
+
+### Available Statistics
+
+The following statistics are available for tracking:
+
+1. Basic Statistics:
+   - `mean`: Mean value
+   - `std`: Standard deviation
+   - `norm`: L2 norm
+   - `min`: Minimum value
+   - `max`: Maximum value
+   - `var`: Variance
+   - `abs_mean`: Mean of absolute values
+
+### Example
+
+See `torch_watcher_example.py` for a complete example showing:
+- How to track all layers (default behavior)
+- How to specify which layers to track
+- How to select statistics to monitor
+- Integration with a training loop
+- Logging metrics to Neptune
+
+## License
+
+MIT License
diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index 7d9dd08..33fe8ba 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -1,5 +1,5 @@
 import warnings
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
 import torch.nn as nn
@@ -15,6 +15,63 @@
     "abs_mean": lambda x: x.abs().mean().item(),
 }
 
+# Common PyTorch layer types for validation
+PYTORCH_LAYERS = {
+    # Linear layers
+    nn.Linear,
+    # Convolutional layers
+    nn.Conv1d,
+    nn.Conv2d,
+    nn.Conv3d,
+    nn.ConvTranspose1d,
+    nn.ConvTranspose2d,
+    nn.ConvTranspose3d,
+    # Recurrent layers
+    nn.LSTM,
+    nn.GRU,
+    nn.RNN,
+    # Normalization layers
+    nn.BatchNorm1d,
+    nn.BatchNorm2d,
+    nn.BatchNorm3d,
+    nn.LayerNorm,
+    nn.InstanceNorm1d,
+    nn.InstanceNorm2d,
+    nn.InstanceNorm3d,
+    # Activation layers
+    nn.ReLU,
+    nn.LeakyReLU,
+    nn.ELU,
+    nn.SELU,
+    nn.GELU,
+    # Pooling layers
+    nn.MaxPool1d,
+    nn.MaxPool2d,
+    nn.MaxPool3d,
+    nn.AvgPool1d,
+    nn.AvgPool2d,
+    nn.AvgPool3d,
+    # Dropout layers
+    nn.Dropout,
+    nn.Dropout2d,
+    nn.Dropout3d,
+    # Embedding layers
+    nn.Embedding,
+    nn.EmbeddingBag,
+    # Transformer layers
+    nn.TransformerEncoderLayer,
+    nn.TransformerDecoderLayer,
+    # Attention layers
+    nn.MultiheadAttention,
+    # Flatten layers
+    nn.Flatten,
+    nn.Unflatten,
+    # Other common layers
+    nn.Sequential,
+    nn.ModuleList,
+    nn.ModuleDict,
+}
+
 
 class HookManager:
     """
@@ -27,33 +84,37 @@ class HookManager:
     - Configurable tracking
     """
 
-    def __init__(self, model: nn.Module, track_layers: Optional[List[type]] = None):
+    def __init__(self, model: nn.Module, track_layers: Optional[List[Type[nn.Module]]] = None):
         """
-        Initialize HookManager with additional configuration options.
+        Initialize HookManager with layer types to track.
 
         Args:
             model (nn.Module): The PyTorch model to track
-            track_layers (Optional[List[type]]): List of layer types to track.
-                                                 Defaults to common layer types if not specified.
+            track_layers (Optional[List[Type[nn.Module]]]): List of PyTorch layer types to track.
+                                                          If None, tracks all layers in the model.
+                                                          If specified, must contain valid PyTorch layer types.
+
+        Raises:
+            TypeError: If model is not a PyTorch model
+            ValueError: If track_layers contains invalid layer types
         """
         if not isinstance(model, nn.Module):
             raise TypeError("The model must be a PyTorch model")
 
+        # Validate that all specified layers are valid PyTorch layers if track_layers is provided
+        if track_layers is not None:
+            invalid_layers = [layer for layer in track_layers if layer not in PYTORCH_LAYERS]
+            if invalid_layers:
+                raise ValueError(
+                    f"Invalid layer types specified: {invalid_layers}. "
+                    f"Please use valid PyTorch layer types from torch.nn."
+                )
+
         self.model = model
         self.hooks: List[torch.utils.hooks.RemovableHandle] = []
         self.activations: Dict[str, torch.Tensor] = {}
         self.gradients: Dict[str, torch.Tensor] = {}
-
-        # Default layer types to track if not specified
-        self.track_layers = track_layers or [
-            nn.Linear,
-            nn.Conv1d,
-            nn.Conv2d,
-            nn.Conv3d,
-            nn.LSTM,
-            nn.GRU,
-            nn.RNN,
-        ]
+        self.track_layers = track_layers
 
     def save_activation(self, name: str):
         """Create a forward hook to save layer activations."""
@@ -94,14 +155,26 @@ def register_hooks(self, track_activations: bool = True, track_gradients: bool =
         # Register forward hooks for activations
         if track_activations:
             for name, module in self.model.named_modules():
-                if any(isinstance(module, layer_type) for layer_type in self.track_layers):
+                # Skip the model itself
+                if name == "":
+                    continue
+                # Track all layers if track_layers is None, otherwise only specified types
+                if self.track_layers is None or any(
+                    isinstance(module, layer_type) for layer_type in self.track_layers
+                ):
                     hook = module.register_forward_hook(self.save_activation(name))
                     self.hooks.append(hook)
 
         # Register backward hooks for gradients
         if track_gradients:
             for name, module in self.model.named_modules():
-                if any(isinstance(module, layer_type) for layer_type in self.track_layers):
+                # Skip the model itself
+                if name == "":
+                    continue
+                # Track all layers if track_layers is None, otherwise only specified types
+                if self.track_layers is None or any(
+                    isinstance(module, layer_type) for layer_type in self.track_layers
+                ):
                     hook = module.register_full_backward_hook(self.save_gradient(name))
                     self.hooks.append(hook)
 
@@ -138,19 +211,25 @@ def __init__(
         self,
         model: nn.Module,
         run: Any,  # Made more flexible to support different logging mechanisms
-        track_layers: Optional[List[type]] = None,
+        track_layers: Optional[List[Type[nn.Module]]] = None,
         tensor_stats: Optional[List[str]] = None,
     ) -> None:
         """
-        Initialize TorchWatcher with more configuration options.
+        Initialize TorchWatcher with configuration options.
 
         Args:
             model (nn.Module): The PyTorch model to watch
             run: Logging mechanism from Neptune
-            track_layers (Optional[List[type]]): Layer types to specifically track
+            track_layers (Optional[List[Type[nn.Module]]]): List of PyTorch layer types to track.
+                                                          If None, tracks all layers in the model.
+                                                          If specified, must contain valid PyTorch layer types.
             tensor_stats (Optional[List[str]]): List of statistics to compute.
                                               Available options: mean, std, norm, min, max, var, abs_mean.
                                               Defaults to ['mean'] if not specified.
+
+        Raises:
+            TypeError: If model is not a PyTorch model
+            ValueError: If track_layers contains invalid layer types
         """
         if not isinstance(model, nn.Module):
             raise TypeError("The model must be a PyTorch model")
diff --git a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
new file mode 100644
index 0000000..55ad415
--- /dev/null
+++ b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
@@ -0,0 +1,120 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from neptune_scale import Run
+from TorchWatcher import TorchWatcher
+
+# Define a simple neural network
+class SimpleNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(10, 5)
+        self.fc2 = nn.Linear(5, 1)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+def generate_data(n_samples=1000):
+    """Generate synthetic data for a regression task."""
+    # Generate random input features
+    X = torch.randn(n_samples, 10)
+    
+    # Create target values with some non-linear relationship
+    y = (X[:, 0] ** 2 + 0.5 * X[:, 1] + 0.1 * torch.sum(X[:, 2:], dim=1)).unsqueeze(1)
+    
+    # Add some noise
+    y += 0.1 * torch.randn_like(y)
+    
+    return X, y
+
+def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, batch_size=32):
+    """Training function that can be used with any watcher configuration."""
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+    criterion = nn.MSELoss()
+    n_batches = len(X_train) // batch_size
+
+    for epoch in range(n_epochs):
+        model.train()
+        train_loss = 0.0
+        
+        # Training batches
+        for i in range(n_batches):
+            start_idx = i * batch_size
+            end_idx = start_idx + batch_size
+            
+            x_batch = X_train[start_idx:end_idx]
+            y_batch = y_train[start_idx:end_idx]
+
+            # Forward pass
+            output = model(x_batch)
+            loss = criterion(output, y_batch)
+
+            # Backward pass
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            # Track metrics after the forward and backward passes
+            watcher.watch(step=epoch * n_batches + i)
+
+            train_loss += loss.item()
+
+        # Average training loss
+        train_loss /= n_batches
+
+        # Validation
+        model.eval()
+        with torch.no_grad():
+            val_output = model(X_val)
+            val_loss = criterion(val_output, y_val)
+
+        # Log metrics
+        watcher.run.log_metrics(data={
+            "train/loss": train_loss,
+            "val/loss": val_loss.item()
+        }, step=epoch)
+
+        if (epoch + 1) % 10 == 0:
+            print(f"Epoch [{epoch+1}/{n_epochs}], "
+                  f"Train Loss: {train_loss:.4f}, "
+                  f"Val Loss: {val_loss.item():.4f}")
+
+def main():
+    # Initialize Neptune run
+    run = Run(
+        experiment_name="torch-watcher-example",
+    )
+
+    # Generate data
+    X_train, y_train = generate_data(n_samples=1000)
+    X_val, y_val = generate_data(n_samples=200)
+
+    # Example 1: Track all layers in the model
+    print("\nTraining with all layers tracked:")
+    model1 = SimpleNet()
+    watcher1 = TorchWatcher(
+        model1,
+        run,
+        tensor_stats=['mean', 'norm']  # track_layers defaults to None, tracking all layers
+    )
+    train_model(model1, X_train, y_train, X_val, y_val, watcher1)
+    watcher1.run.close()
+
+    # Example 2: Track specific layer types
+    print("\nTraining with only Linear and ReLU layers tracked:")
+    model2 = SimpleNet()
+    run2 = Run(experiment_name="torch-watcher-example-specific")
+    watcher2 = TorchWatcher(
+        model2,
+        run2,
+        track_layers=[nn.Linear, nn.ReLU],  # Only track Linear and ReLU layers
+        tensor_stats=['mean', 'norm']
+    )
+    train_model(model2, X_train, y_train, X_val, y_val, watcher2)
+    watcher2.run.close()
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file

From 84a7ca334d1ab81d1e5800a83cc1107702581de8 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 31 Mar 2025 18:58:59 +0200
Subject: [PATCH 080/125] chore: update readme

---
 integrations-and-supported-tools/pytorch/notebooks/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/README.md b/integrations-and-supported-tools/pytorch/notebooks/README.md
index 90c2872..a4b126a 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/README.md
+++ b/integrations-and-supported-tools/pytorch/notebooks/README.md
@@ -20,7 +20,7 @@ A simple PyTorch hook manager for tracking model metrics during training.
 ## Installation
 
 ```bash
-pip install neptune-client
+pip install neptune_scale
 ```
 
 ## Usage

From 75265eccfa7c780ed76ae61f71a0100588b2a46b Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 09:59:14 +0200
Subject: [PATCH 081/125] chore: cleanup comments and add TODO for future
 improvements

---
 .../pytorch/notebooks/TorchWatcher.py                          | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index 33fe8ba..5d131b8 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -204,7 +204,7 @@ def __del__(self):
 
 class TorchWatcher:
     """
-    A comprehensive tracking mechanism for PyTorch models with enhanced logging and context management.
+    A comprehensive tracking mechanism for PyTorch models with enhanced logging.
     """
 
     def __init__(
@@ -244,6 +244,7 @@ def __init__(
             tensor_stats = ["mean"]
 
         # Validate that all requested statistics exist
+        # TODO: Add ability to set custom statistics
         invalid_stats = [stat for stat in tensor_stats if stat not in TENSOR_STATS]
         if invalid_stats:
             raise ValueError(

From ebde389e6ff47e8619534de445fd92a573b8b786 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 10:02:51 +0200
Subject: [PATCH 082/125] refactor: update TorchWatcher to use a common
 _track_metrics() method

---
 .../pytorch/notebooks/TorchWatcher.py         | 36 ++++++++++--------
 .../notebooks/torch_watcher_example.py        | 38 +++++++++++--------
 2 files changed, 43 insertions(+), 31 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index 5d131b8..3269c1a 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -275,31 +275,37 @@ def _safe_tensor_stats(self, tensor: torch.Tensor) -> Dict[str, float]:
                 warnings.warn(f"Could not compute {stat_name} statistic: {e}")
         return stats
 
+    def _track_metric(self, metric_type: str, data: Dict[str, torch.Tensor]):
+        """Track metrics with enhanced statistics for a given metric type.
+
+        Args:
+            metric_type (str): Type of metric being tracked (activation/gradient/parameters)
+            data (Dict[str, torch.Tensor]): Dictionary mapping layer names to tensors
+        """
+        for layer, tensor in data.items():
+            if tensor is not None:
+                stats = self._safe_tensor_stats(tensor)
+                for stat_name, stat_value in stats.items():
+                    self.debug_metrics[f"debug/{metric_type}/{layer}_{stat_name}"] = stat_value
+
     def track_activations(self):
         """Track layer activations with enhanced statistics."""
         activations = self.hm.get_activations()
-        for layer, activation in activations.items():
-            if activation is not None:
-                stats = self._safe_tensor_stats(activation)
-                for stat_name, stat_value in stats.items():
-                    self.debug_metrics[f"debug/activation/{layer}_{stat_name}"] = stat_value
+        self._track_metric("activation", activations)
 
     def track_gradients(self):
         """Track layer gradients with enhanced statistics."""
         gradients = self.hm.get_gradients()
-        for layer, gradient in gradients.items():
-            if gradient is not None:
-                stats = self._safe_tensor_stats(gradient)
-                for stat_name, stat_value in stats.items():
-                    self.debug_metrics[f"debug/gradient/{layer}_{stat_name}"] = stat_value
+        self._track_metric("gradient", gradients)
 
     def track_parameters(self):
         """Track model parameters with enhanced statistics."""
-        for layer, param in self.model.named_parameters():
-            if param is not None and param.grad is not None:
-                stats = self._safe_tensor_stats(param.grad)
-                for stat_name, stat_value in stats.items():
-                    self.debug_metrics[f"debug/parameters/{layer}_{stat_name}"] = stat_value
+        parameters = {
+            name: param.grad
+            for name, param in self.model.named_parameters()
+            if param is not None and param.grad is not None
+        }
+        self._track_metric("parameters", parameters)
 
     def watch(
         self,
diff --git a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
index 55ad415..f1a52f8 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
@@ -1,9 +1,10 @@
+import numpy as np
 import torch
 import torch.nn as nn
-import numpy as np
 from neptune_scale import Run
 from TorchWatcher import TorchWatcher
 
+
 # Define a simple neural network
 class SimpleNet(nn.Module):
     def __init__(self):
@@ -17,19 +18,21 @@ def forward(self, x):
         x = self.fc2(x)
         return x
 
+
 def generate_data(n_samples=1000):
     """Generate synthetic data for a regression task."""
     # Generate random input features
     X = torch.randn(n_samples, 10)
-    
+
     # Create target values with some non-linear relationship
     y = (X[:, 0] ** 2 + 0.5 * X[:, 1] + 0.1 * torch.sum(X[:, 2:], dim=1)).unsqueeze(1)
-    
+
     # Add some noise
     y += 0.1 * torch.randn_like(y)
-    
+
     return X, y
 
+
 def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, batch_size=32):
     """Training function that can be used with any watcher configuration."""
     optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
@@ -39,12 +42,12 @@ def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, bat
     for epoch in range(n_epochs):
         model.train()
         train_loss = 0.0
-        
+
         # Training batches
         for i in range(n_batches):
             start_idx = i * batch_size
             end_idx = start_idx + batch_size
-            
+
             x_batch = X_train[start_idx:end_idx]
             y_batch = y_train[start_idx:end_idx]
 
@@ -72,15 +75,17 @@ def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, bat
             val_loss = criterion(val_output, y_val)
 
         # Log metrics
-        watcher.run.log_metrics(data={
-            "train/loss": train_loss,
-            "val/loss": val_loss.item()
-        }, step=epoch)
+        watcher.run.log_metrics(
+            data={"train/loss": train_loss, "val/loss": val_loss.item()}, step=epoch
+        )
 
         if (epoch + 1) % 10 == 0:
-            print(f"Epoch [{epoch+1}/{n_epochs}], "
-                  f"Train Loss: {train_loss:.4f}, "
-                  f"Val Loss: {val_loss.item():.4f}")
+            print(
+                f"Epoch [{epoch+1}/{n_epochs}], "
+                f"Train Loss: {train_loss:.4f}, "
+                f"Val Loss: {val_loss.item():.4f}"
+            )
+
 
 def main():
     # Initialize Neptune run
@@ -98,7 +103,7 @@ def main():
     watcher1 = TorchWatcher(
         model1,
         run,
-        tensor_stats=['mean', 'norm']  # track_layers defaults to None, tracking all layers
+        tensor_stats=["mean", "norm"],  # track_layers defaults to None, tracking all layers
     )
     train_model(model1, X_train, y_train, X_val, y_val, watcher1)
     watcher1.run.close()
@@ -111,10 +116,11 @@ def main():
         model2,
         run2,
         track_layers=[nn.Linear, nn.ReLU],  # Only track Linear and ReLU layers
-        tensor_stats=['mean', 'norm']
+        tensor_stats=["mean", "norm"],
     )
     train_model(model2, X_train, y_train, X_val, y_val, watcher2)
     watcher2.run.close()
 
+
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()

From 9f83fbf074091d7d565661c269b5c25a237caa32 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 13:55:23 +0200
Subject: [PATCH 083/125] refactor: update the watch() method to accept boolen
 inputs for the metrics to track rather than named values

---
 .../pytorch/notebooks/TorchWatcher.py         | 23 ++++++++-----------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index 3269c1a..3efd1b0 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -310,32 +310,29 @@ def track_parameters(self):
     def watch(
         self,
         step: Union[int, float],
-        log: Optional[List[Literal["gradients", "parameters", "activations"]]] = None,
+        track_gradients: bool = True,
+        track_parameters: bool = True, 
+        track_activations: bool = True
     ):
         """
         Log debug metrics with flexible configuration.
 
         Args:
             step (int|float): Logging step
-            log (Optional[List]): Specific tracking modes.
-                                  Defaults to all if None.
+            track_gradients (bool): Whether to track gradients. Defaults to True.
+            track_parameters (bool): Whether to track parameters. Defaults to True.
+            track_activations (bool): Whether to track activations. Defaults to True.
         """
         # Reset metrics
         self.debug_metrics.clear()
 
-        # Determine tracking modes
-        if log is None or log == "all":
+        # Track metrics based on boolean flags
+        if track_gradients:
             self.track_gradients()
+        if track_parameters:
             self.track_parameters()
+        if track_activations:
             self.track_activations()
-        else:
-            for mode in log:
-                if mode == "gradients":
-                    self.track_gradients()
-                elif mode == "parameters":
-                    self.track_parameters()
-                elif mode == "activations":
-                    self.track_activations()
 
         # Log metrics
         try:

From 603af72be2cd1c27c9b37069b50b8c0c647e8fc2 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 14:53:27 +0200
Subject: [PATCH 084/125] feat: add ability to specify base_namespace and
 namespace during training loop.

- more control on namespace logged during training
---
 .../pytorch/notebooks/TorchWatcher.py         | 34 ++++++++++++-------
 1 file changed, 22 insertions(+), 12 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index 3efd1b0..5ba6c87 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -213,6 +213,7 @@ def __init__(
         run: Any,  # Made more flexible to support different logging mechanisms
         track_layers: Optional[List[Type[nn.Module]]] = None,
         tensor_stats: Optional[List[str]] = None,
+        base_namespace: str = "debug",  # Default namespace for all metrics
     ) -> None:
         """
         Initialize TorchWatcher with configuration options.
@@ -226,6 +227,7 @@ def __init__(
             tensor_stats (Optional[List[str]]): List of statistics to compute.
                                               Available options: mean, std, norm, min, max, var, abs_mean.
                                               Defaults to ['mean'] if not specified.
+            base_namespace (str): Base namespace for all logged metrics. Defaults to "debug".
 
         Raises:
             TypeError: If model is not a PyTorch model
@@ -238,6 +240,7 @@ def __init__(
         self.run = run
         self.hm = HookManager(model, track_layers)
         self.debug_metrics: Dict[str, float] = {}
+        self.base_namespace = base_namespace
 
         # Validate and set tensor statistics
         if tensor_stats is None:
@@ -275,44 +278,49 @@ def _safe_tensor_stats(self, tensor: torch.Tensor) -> Dict[str, float]:
                 warnings.warn(f"Could not compute {stat_name} statistic: {e}")
         return stats
 
-    def _track_metric(self, metric_type: str, data: Dict[str, torch.Tensor]):
+    def _track_metric(self, metric_type: str, data: Dict[str, torch.Tensor], namespace: Optional[str] = None):
         """Track metrics with enhanced statistics for a given metric type.
 
         Args:
             metric_type (str): Type of metric being tracked (activation/gradient/parameters)
             data (Dict[str, torch.Tensor]): Dictionary mapping layer names to tensors
+            namespace (Optional[str]): Optional namespace to prefix the base namespace
         """
+        # Construct the full namespace
+        full_namespace = f"{namespace}/{self.base_namespace}" if namespace else self.base_namespace
+        
         for layer, tensor in data.items():
             if tensor is not None:
                 stats = self._safe_tensor_stats(tensor)
                 for stat_name, stat_value in stats.items():
-                    self.debug_metrics[f"debug/{metric_type}/{layer}_{stat_name}"] = stat_value
+                    self.debug_metrics[f"{full_namespace}/{metric_type}/{layer}_{stat_name}"] = stat_value
 
-    def track_activations(self):
+    def track_activations(self, namespace: Optional[str] = None):
         """Track layer activations with enhanced statistics."""
         activations = self.hm.get_activations()
-        self._track_metric("activation", activations)
+        self._track_metric("activation", activations, namespace)
 
-    def track_gradients(self):
+    def track_gradients(self, namespace: Optional[str] = None):
         """Track layer gradients with enhanced statistics."""
         gradients = self.hm.get_gradients()
-        self._track_metric("gradient", gradients)
+        self._track_metric("gradient", gradients, namespace)
 
-    def track_parameters(self):
+    def track_parameters(self, namespace: Optional[str] = None):
         """Track model parameters with enhanced statistics."""
         parameters = {
             name: param.grad
             for name, param in self.model.named_parameters()
             if param is not None and param.grad is not None
         }
-        self._track_metric("parameters", parameters)
+        self._track_metric("parameters", parameters, namespace)
 
     def watch(
         self,
         step: Union[int, float],
         track_gradients: bool = True,
         track_parameters: bool = True, 
-        track_activations: bool = True
+        track_activations: bool = True,
+        namespace: Optional[str] = None,
     ):
         """
         Log debug metrics with flexible configuration.
@@ -322,17 +330,19 @@ def watch(
             track_gradients (bool): Whether to track gradients. Defaults to True.
             track_parameters (bool): Whether to track parameters. Defaults to True.
             track_activations (bool): Whether to track activations. Defaults to True.
+            namespace (Optional[str]): Optional namespace to prefix the base namespace.
+                                     If provided, metrics will be logged under {namespace}/{base_namespace}/...
         """
         # Reset metrics
         self.debug_metrics.clear()
 
         # Track metrics based on boolean flags
         if track_gradients:
-            self.track_gradients()
+            self.track_gradients(namespace)
         if track_parameters:
-            self.track_parameters()
+            self.track_parameters(namespace)
         if track_activations:
-            self.track_activations()
+            self.track_activations(namespace)
 
         # Log metrics
         try:

From 6bb48799780fb60dc3788c6dad338be1d0059516 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 14:53:55 +0200
Subject: [PATCH 085/125] chore: updare readme and example for namespace
 feature

---
 .../pytorch/notebooks/README.md               | 253 ++++++++----------
 .../notebooks/torch_watcher_example.py        |  83 +++---
 2 files changed, 159 insertions(+), 177 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/README.md b/integrations-and-supported-tools/pytorch/notebooks/README.md
index a4b126a..aa383a7 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/README.md
+++ b/integrations-and-supported-tools/pytorch/notebooks/README.md
@@ -1,21 +1,16 @@
 # TorchWatcher
 
-A simple PyTorch hook manager for tracking model metrics during training.
+A lightweight PyTorch model monitoring tool that automatically tracks layer activations, gradients, and parameters during training. Built for seamless integration with Neptune.ai.
 
 ## Features
 
-- Track predefined statistics for specified PyTorch layers
-- Automatic metric logging to Neptune
-- Support for both training and validation phases
-- Selective layer tracking
-- Predefined statistics options:
-  - `mean`: Mean value
-  - `std`: Standard deviation
-  - `norm`: L2 norm
-  - `min`: Minimum value
-  - `max`: Maximum value
-  - `var`: Variance
-  - `abs_mean`: Mean of absolute values
+- **Automatic Layer Tracking**: Monitors activations, gradients, and parameters of specified PyTorch layers
+- **Flexible Layer Selection**: Track all layers or specify which layer types to monitor
+- **Comprehensive Statistics**: Predefined tensor statistics including mean, standard deviation, norm, min, max, variance, and absolute mean
+- **Configurable Tracking**: Enable/disable tracking of activations, gradients, and parameters as needed
+- **Organized Logging**: Structured metric namespacing for better organization in Neptune
+- **Memory Efficient**: Clears stored tensors after each logging step
+- **Error Handling**: Robust error handling with informative warnings
 
 ## Installation
 
@@ -32,184 +27,154 @@ from neptune_scale import Run
 from TorchWatcher import TorchWatcher
 
 # Initialize Neptune run
-run = Run(
-    experiment_name="my-experiment",
-)
+run = Run(experiment_name="my-experiment")
 
-# Create your model
+# Create your PyTorch model
 model = YourModel()
 
-# Option 1: Track all layers (default)
-watcher = TorchWatcher(
-    model,
-    run,
-    tensor_stats=['mean', 'norm']   # Optional: select from predefined statistics
-)
-
-# Option 2: Track specific layer types
-layers_to_track = [
-    nn.Linear,    # Track all linear layers
-    nn.Conv2d,    # Track all convolutional layers
-    nn.ReLU,      # Track all ReLU layers
-]
-
+# Initialize TorchWatcher
 watcher = TorchWatcher(
     model,
     run,
-    track_layers=layers_to_track,  # Specify which layers to track
-    tensor_stats=['mean', 'norm']   # Optional: select from predefined statistics
+    track_layers=[nn.Linear, nn.ReLU],  # Specify which layer types to track (default is all layers)
+    tensor_stats=['mean', 'norm'],       # Choose which statistics to compute (default = "mean" only)
+    base_namespace="model_metrics"       # Set base namespace for all metrics (default = "debug")
 )
 
 # Training loop
 for epoch in range(n_epochs):
-    for batch in dataloader:
+    for batch in train_loader:
         # Forward pass
         output = model(batch)
         
         # Backward pass
         loss.backward()
         
-        # Track metrics
+        # Track metrics with default namespace
         watcher.watch(step=current_step)
 ```
 
-### Detailed Examples
-
-#### 1. Using Default Settings
+### Controlling Metric Logging
 
-The simplest way to use TorchWatcher is with default settings, which will track all layers in your model:
+The `watch()` method provides flexible control over what metrics to track and how to organize them:
 
 ```python
-# Initialize with defaults
-watcher = TorchWatcher(
-    model,
-    run
+# Track all metrics (default behavior)
+watcher.watch(step=current_step)
+
+# Track specific metrics
+watcher.watch(
+    step=current_step,
+    track_activations=True,
+    track_gradients=False,
+    track_parameters=True
 )
 
-# This will:
-# - Track all layers in the model
-# - Use only the 'mean' statistic (default)
-# - Log all metrics (activations, gradients, parameters)
+# Use a namespace to organize metrics
+watcher.watch(
+    step=current_step,
+    namespace="train"  # Metrics will be under "train/model_metrics/..."
+)
 ```
 
-#### 2. Specifying Which Layers to Track
+### Namespace Organization
 
-You can choose to track specific layer types in your model:
+TorchWatcher provides a hierarchical namespace structure for organizing metrics:
 
+1. **Base Namespace**: Set during initialization
 ```python
-# Track only convolutional and linear layers
-watcher = TorchWatcher(
-    model,
-    run,
-    track_layers=[nn.Conv2d, nn.Linear]
-)
-
-# Track only normalization layers
 watcher = TorchWatcher(
     model,
     run,
-    track_layers=[nn.BatchNorm2d, nn.LayerNorm]
-)
-
-# Track only activation layers
-watcher = TorchWatcher(
-    model,
-    run,
-    track_layers=[nn.ReLU, nn.LeakyReLU, nn.GELU]
+    base_namespace="model_metrics"  # All metrics will be under "model_metrics/"
 )
 ```
 
-#### 3. Selecting Statistics to Capture
-
-You can specify which statistics to compute for each layer:
-
+2. **Per-Call Namespace**: Prefix for specific tracking calls
 ```python
-# Track multiple statistics
-watcher = TorchWatcher(
-    model,
-    run,
-    tensor_stats=['mean', 'std', 'norm', 'min', 'max']
-)
+# During training
+watcher.watch(step=step, namespace="train")  # Metrics under "train/model_metrics/"
 
-# Track only basic statistics
-watcher = TorchWatcher(
-    model,
-    run,
-    tensor_stats=['mean', 'std']
-)
+# During validation
+watcher.watch(step=step, namespace="validation")  # Metrics under "validation/model_metrics/"
+```
 
-# Track only norm-based statistics
-watcher = TorchWatcher(
-    model,
-    run,
-    tensor_stats=['norm', 'abs_mean']
-)
+3. **Metric Structure**: Metrics are organized as:
+```
+{namespace}/{base_namespace}/{metric_type}/{layer_name}_{statistic}
 ```
 
-#### 4. Controlling Metric Logging
+Example metric names:
+- `train/model_metrics/activation/fc1_mean`
+- `validation/model_metrics/gradient/fc2_norm`
+- `train/model_metrics/parameters/fc1_weight_mean`
 
-The `watch()` method allows you to specify which metrics to log:
+### Example Use Cases
 
+1. **Training with Full Tracking**:
 ```python
-# Log all metrics (default)
-watcher.watch(step=current_step)
-
-# Log only activation metrics
-watcher.watch(step=current_step, log=["activations"])
-
-# Log only gradient metrics
-watcher.watch(step=current_step, log=["gradients"])
+# Track everything during initial training
+watcher.watch(step=step, namespace="train")
+```
 
-# Log only parameter metrics
-watcher.watch(step=current_step, log=["parameters"])
+2. **Validation with Limited Tracking**:
+```python
+# Track only activations during validation
+watcher.watch(
+    step=step,
+    track_activations=True,
+    track_gradients=False,
+    track_parameters=False,
+    namespace="validation"
+)
+```
 
-# Log specific combinations
-watcher.watch(step=current_step, log=["activations", "gradients"])
+3. **Efficient Training**:
+```python
+# Track only gradients during later training phases
+watcher.watch(
+    step=step,
+    track_activations=False,
+    track_parameters=False,
+    track_gradients=True,
+    namespace="train"
+)
 ```
 
-### Layer Types
-
-You can track any of these PyTorch layer types:
-- `nn.Linear`
-- `nn.Conv1d`, `nn.Conv2d`, `nn.Conv3d`
-- `nn.ConvTranspose1d`, `nn.ConvTranspose2d`, `nn.ConvTranspose3d`
-- `nn.BatchNorm1d`, `nn.BatchNorm2d`, `nn.BatchNorm3d`
-- `nn.LayerNorm`
-- `nn.InstanceNorm1d`, `nn.InstanceNorm2d`, `nn.InstanceNorm3d`
-- `nn.GroupNorm`
-- `nn.ReLU`, `nn.LeakyReLU`, `nn.PReLU`, `nn.RReLU`, `nn.ELU`, `nn.SELU`, `nn.CELU`, `nn.GELU`
-- `nn.Sigmoid`
-- `nn.Tanh`
-- `nn.Dropout`, `nn.Dropout2d`, `nn.Dropout3d`
-- `nn.MaxPool1d`, `nn.MaxPool2d`, `nn.MaxPool3d`
-- `nn.AvgPool1d`, `nn.AvgPool2d`, `nn.AvgPool3d`
-- `nn.AdaptiveMaxPool1d`, `nn.AdaptiveMaxPool2d`, `nn.AdaptiveMaxPool3d`
-- `nn.AdaptiveAvgPool1d`, `nn.AdaptiveAvgPool2d`, `nn.AdaptiveAvgPool3d`
-- `nn.LSTM`, `nn.GRU`, `nn.RNN`
-- `nn.Embedding`
-- `nn.TransformerEncoderLayer`, `nn.TransformerDecoderLayer`
-
-### Available Statistics
-
-The following statistics are available for tracking:
-
-1. Basic Statistics:
-   - `mean`: Mean value
-   - `std`: Standard deviation
-   - `norm`: L2 norm
-   - `min`: Minimum value
-   - `max`: Maximum value
-   - `var`: Variance
-   - `abs_mean`: Mean of absolute values
-
-### Example
-
-See `torch_watcher_example.py` for a complete example showing:
-- How to track all layers (default behavior)
-- How to specify which layers to track
-- How to select statistics to monitor
-- Integration with a training loop
-- Logging metrics to Neptune
+## Supported Layer Types
+
+TorchWatcher supports tracking of all common PyTorch layer types, including:
+- Linear layers
+- Convolutional layers
+- Recurrent layers
+- Normalization layers
+- Activation layers
+- Pooling layers
+- Dropout layers
+- Embedding layers
+- Transformer layers
+- Attention layers
+- And more...
+
+## Available Statistics
+
+Predefined tensor statistics include:
+- `mean`: Mean value
+- `std`: Standard deviation
+- `norm`: L2 norm
+- `min`: Minimum value
+- `max`: Maximum value
+- `var`: Variance
+- `abs_mean`: Mean of absolute values
+
+## Example
+
+See `torch_watcher_example.py` for a complete example demonstrating:
+- Model definition
+- Data generation
+- Training loop with different tracking configurations
+- Namespace organization
+- Integration with Neptune
 
 ## License
 
diff --git a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
index f1a52f8..a173d4e 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
@@ -1,6 +1,6 @@
-import numpy as np
 import torch
 import torch.nn as nn
+import numpy as np
 from neptune_scale import Run
 from TorchWatcher import TorchWatcher
 
@@ -34,7 +34,7 @@ def generate_data(n_samples=1000):
 
 
 def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, batch_size=32):
-    """Training function that can be used with any watcher configuration."""
+    """Training function demonstrating different ways to use TorchWatcher."""
     optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
     criterion = nn.MSELoss()
     n_batches = len(X_train) // batch_size
@@ -60,8 +60,17 @@ def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, bat
             loss.backward()
             optimizer.step()
 
-            # Track metrics after the forward and backward passes
-            watcher.watch(step=epoch * n_batches + i)
+            # Track metrics during training
+            if epoch < 5:  # First 5 epochs: track everything
+                watcher.watch(step=epoch * n_batches + i, namespace="train")
+            else:  # Later epochs: track only gradients for efficiency
+                watcher.watch(
+                    step=epoch * n_batches + i,
+                    track_activations=False,
+                    track_parameters=False,
+                    track_gradients=True,
+                    namespace="train"
+                )
 
             train_loss += loss.item()
 
@@ -73,18 +82,26 @@ def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, bat
         with torch.no_grad():
             val_output = model(X_val)
             val_loss = criterion(val_output, y_val)
+            
+            # Track metrics during validation
+            watcher.watch(
+                step=epoch,
+                track_activations=True,
+                track_gradients=False,
+                track_parameters=False,
+                namespace="validation"
+            )
 
         # Log metrics
-        watcher.run.log_metrics(
-            data={"train/loss": train_loss, "val/loss": val_loss.item()}, step=epoch
-        )
+        watcher.run.log_metrics(data={
+            "train/loss": train_loss,
+            "val/loss": val_loss.item()
+        }, step=epoch)
 
         if (epoch + 1) % 10 == 0:
-            print(
-                f"Epoch [{epoch+1}/{n_epochs}], "
-                f"Train Loss: {train_loss:.4f}, "
-                f"Val Loss: {val_loss.item():.4f}"
-            )
+            print(f"Epoch [{epoch+1}/{n_epochs}], "
+                  f"Train Loss: {train_loss:.4f}, "
+                  f"Val Loss: {val_loss.item():.4f}")
 
 
 def main():
@@ -97,29 +114,29 @@ def main():
     X_train, y_train = generate_data(n_samples=1000)
     X_val, y_val = generate_data(n_samples=200)
 
-    # Example 1: Track all layers in the model
-    print("\nTraining with all layers tracked:")
-    model1 = SimpleNet()
-    watcher1 = TorchWatcher(
-        model1,
+    # Create model and watcher
+    model = SimpleNet()
+    
+    # Initialize watcher with specific layer types to track
+    watcher = TorchWatcher(
+        model,
         run,
-        tensor_stats=["mean", "norm"],  # track_layers defaults to None, tracking all layers
-    )
-    train_model(model1, X_train, y_train, X_val, y_val, watcher1)
-    watcher1.run.close()
-
-    # Example 2: Track specific layer types
-    print("\nTraining with only Linear and ReLU layers tracked:")
-    model2 = SimpleNet()
-    run2 = Run(experiment_name="torch-watcher-example-specific")
-    watcher2 = TorchWatcher(
-        model2,
-        run2,
-        track_layers=[nn.Linear, nn.ReLU],  # Only track Linear and ReLU layers
-        tensor_stats=["mean", "norm"],
+        track_layers=[nn.Linear, nn.ReLU],  # Track only Linear and ReLU layers
+        tensor_stats=['mean', 'norm'],       # Track mean and norm statistics
+        base_namespace="model_metrics"       # Default namespace for all metrics
     )
-    train_model(model2, X_train, y_train, X_val, y_val, watcher2)
-    watcher2.run.close()
+
+    # Train the model
+    print("\nTraining with TorchWatcher:")
+    print("- Tracking Linear and ReLU layers")
+    print("- Computing mean and norm statistics")
+    print("- Using 'model_metrics' as base namespace")
+    print("- Full tracking during first 5 epochs with 'train/model_metrics' namespace")
+    print("- Gradient-only tracking during later epochs with 'train/model_metrics' namespace")
+    print("- Activation-only tracking during validation with 'validation/model_metrics' namespace")
+    
+    train_model(model, X_train, y_train, X_val, y_val, watcher)
+    watcher.run.close()
 
 
 if __name__ == "__main__":

From 50b0a6d90742885cec4eab15a02946223ea74e09 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 14:55:19 +0200
Subject: [PATCH 086/125] chore: pre-commit hooks changes

---
 .../pytorch/notebooks/README.md               |  4 +--
 .../pytorch/notebooks/TorchWatcher.py         | 12 ++++---
 .../notebooks/torch_watcher_example.py        | 31 ++++++++++---------
 3 files changed, 26 insertions(+), 21 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/README.md b/integrations-and-supported-tools/pytorch/notebooks/README.md
index aa383a7..67e6bd8 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/README.md
+++ b/integrations-and-supported-tools/pytorch/notebooks/README.md
@@ -46,10 +46,10 @@ for epoch in range(n_epochs):
     for batch in train_loader:
         # Forward pass
         output = model(batch)
-        
+
         # Backward pass
         loss.backward()
-        
+
         # Track metrics with default namespace
         watcher.watch(step=current_step)
 ```
diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
index 5ba6c87..6693859 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
@@ -278,7 +278,9 @@ def _safe_tensor_stats(self, tensor: torch.Tensor) -> Dict[str, float]:
                 warnings.warn(f"Could not compute {stat_name} statistic: {e}")
         return stats
 
-    def _track_metric(self, metric_type: str, data: Dict[str, torch.Tensor], namespace: Optional[str] = None):
+    def _track_metric(
+        self, metric_type: str, data: Dict[str, torch.Tensor], namespace: Optional[str] = None
+    ):
         """Track metrics with enhanced statistics for a given metric type.
 
         Args:
@@ -288,12 +290,14 @@ def _track_metric(self, metric_type: str, data: Dict[str, torch.Tensor], namespa
         """
         # Construct the full namespace
         full_namespace = f"{namespace}/{self.base_namespace}" if namespace else self.base_namespace
-        
+
         for layer, tensor in data.items():
             if tensor is not None:
                 stats = self._safe_tensor_stats(tensor)
                 for stat_name, stat_value in stats.items():
-                    self.debug_metrics[f"{full_namespace}/{metric_type}/{layer}_{stat_name}"] = stat_value
+                    self.debug_metrics[f"{full_namespace}/{metric_type}/{layer}_{stat_name}"] = (
+                        stat_value
+                    )
 
     def track_activations(self, namespace: Optional[str] = None):
         """Track layer activations with enhanced statistics."""
@@ -318,7 +322,7 @@ def watch(
         self,
         step: Union[int, float],
         track_gradients: bool = True,
-        track_parameters: bool = True, 
+        track_parameters: bool = True,
         track_activations: bool = True,
         namespace: Optional[str] = None,
     ):
diff --git a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
index a173d4e..1addce3 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
+++ b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
@@ -1,6 +1,6 @@
+import numpy as np
 import torch
 import torch.nn as nn
-import numpy as np
 from neptune_scale import Run
 from TorchWatcher import TorchWatcher
 
@@ -69,7 +69,7 @@ def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, bat
                     track_activations=False,
                     track_parameters=False,
                     track_gradients=True,
-                    namespace="train"
+                    namespace="train",
                 )
 
             train_loss += loss.item()
@@ -82,26 +82,27 @@ def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, bat
         with torch.no_grad():
             val_output = model(X_val)
             val_loss = criterion(val_output, y_val)
-            
+
             # Track metrics during validation
             watcher.watch(
                 step=epoch,
                 track_activations=True,
                 track_gradients=False,
                 track_parameters=False,
-                namespace="validation"
+                namespace="validation",
             )
 
         # Log metrics
-        watcher.run.log_metrics(data={
-            "train/loss": train_loss,
-            "val/loss": val_loss.item()
-        }, step=epoch)
+        watcher.run.log_metrics(
+            data={"train/loss": train_loss, "val/loss": val_loss.item()}, step=epoch
+        )
 
         if (epoch + 1) % 10 == 0:
-            print(f"Epoch [{epoch+1}/{n_epochs}], "
-                  f"Train Loss: {train_loss:.4f}, "
-                  f"Val Loss: {val_loss.item():.4f}")
+            print(
+                f"Epoch [{epoch+1}/{n_epochs}], "
+                f"Train Loss: {train_loss:.4f}, "
+                f"Val Loss: {val_loss.item():.4f}"
+            )
 
 
 def main():
@@ -116,14 +117,14 @@ def main():
 
     # Create model and watcher
     model = SimpleNet()
-    
+
     # Initialize watcher with specific layer types to track
     watcher = TorchWatcher(
         model,
         run,
         track_layers=[nn.Linear, nn.ReLU],  # Track only Linear and ReLU layers
-        tensor_stats=['mean', 'norm'],       # Track mean and norm statistics
-        base_namespace="model_metrics"       # Default namespace for all metrics
+        tensor_stats=["mean", "norm"],  # Track mean and norm statistics
+        base_namespace="model_metrics",  # Default namespace for all metrics
     )
 
     # Train the model
@@ -134,7 +135,7 @@ def main():
     print("- Full tracking during first 5 epochs with 'train/model_metrics' namespace")
     print("- Gradient-only tracking during later epochs with 'train/model_metrics' namespace")
     print("- Activation-only tracking during validation with 'validation/model_metrics' namespace")
-    
+
     train_model(model, X_train, y_train, X_val, y_val, watcher)
     watcher.run.close()
 

From e67e1f6fdbfd59b1929acb030d360758c96c9f91 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 1 Apr 2025 15:39:25 +0200
Subject: [PATCH 087/125] refactor: update example to use the improved
 TorchWatcher package

---
 .../pytorch_text_model_debugging.ipynb        | 110 ++++++++++++++----
 1 file changed, 87 insertions(+), 23 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 073217c..8118620 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -102,9 +102,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -116,9 +125,7 @@
     "from datasets import load_dataset\n",
     "\n",
     "from neptune_scale import Run\n",
-    "import os\n",
-    "\n",
-    "from typing import Literal"
+    "import os"
    ]
   },
   {
@@ -130,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,9 +171,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 81926 \n",
+      "Validation samples: 935\n"
+     ]
+    }
+   ],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
     "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
@@ -196,9 +212,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary size: 128257\n"
+     ]
+    }
+   ],
    "source": [
     "train_subset = data_subset[\"train\"].with_format(\n",
     "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
@@ -233,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,9 +356,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment\n"
+     ]
+    }
+   ],
    "source": [
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
@@ -376,13 +408,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 1 / 10241, Loss: 11.765020370483398\n",
+      "Step 2 / 10241, Loss: 11.628341674804688\n",
+      "Step 3 / 10241, Loss: 9.686809539794922\n",
+      "Step 4 / 10241, Loss: 9.657011032104492\n",
+      "Step 5 / 10241, Loss: 9.74715518951416\n",
+      "Step 6 / 10241, Loss: 10.450525283813477\n",
+      "Step 7 / 10241, Loss: 10.163161277770996\n",
+      "Step 8 / 10241, Loss: 10.278069496154785\n",
+      "Step 9 / 10241, Loss: 10.855117797851562\n",
+      "Step 10 / 10241, Loss: 10.982325553894043\n",
+      "Step 11 / 10241, Loss: 10.332056999206543\n",
+      "Step 12 / 10241, Loss: 10.331168174743652\n",
+      "Step 13 / 10241, Loss: 10.291738510131836\n",
+      "Step 14 / 10241, Loss: 9.950544357299805\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[16], line 36\u001b[0m\n\u001b[0;32m     33\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     35\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[1;32m---> 36\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     37\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     39\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
-    "# Training setup\n",
-    "debug_metrics = {}\n",
-    "\n",
     "# Initialize model and optimizer\n",
     "model = SimpleLLM(\n",
     "    params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"]\n",
@@ -393,7 +458,7 @@
     ")  # Ignore the buffering index of -100 in the dataset\n",
     "\n",
     "# Define watcher class\n",
-    "watcher = TorchWatcher(model=model, run=run, tensor_stats=[\"mean\", \"norm\"])\n",
+    "watcher = TorchWatcher(model=model, run=run, tensor_stats=[\"mean\", \"norm\"], base_namespace=\"debug_metrics\")\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model.to(device)\n",
@@ -425,7 +490,7 @@
     "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
     "\n",
     "        # Call watch() method in loop determing which layer-wise metrics to watch\n",
-    "        watcher.watch(step=step_counter)\n",
+    "        watcher.watch(step=step_counter, track_activations=False, track_parameters=False)\n",
     "\n",
     "        if step_counter % 50 == 0:  # Log validation loss at every 50 steps\n",
     "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",
@@ -435,13 +500,12 @@
     "                    \"metrics/train/loss\": loss.item(),\n",
     "                    \"metrics/validation/loss\": val_loss,\n",
     "                    \"epoch/value\": epoch,\n",
-    "                    **debug_metrics,\n",
     "                },\n",
     "                step=step_counter,\n",
     "            )\n",
     "        else:  # Log training loss and debugging metrics for each step\n",
     "            run.log_metrics(\n",
-    "                data={\"metrics/train/loss\": loss.item(), \"epoch/value\": epoch, **debug_metrics},\n",
+    "                data={\"metrics/train/loss\": loss.item(), \"epoch/value\": epoch},\n",
     "                step=step_counter,\n",
     "            )\n",
     "\n",

From a2d0d5007a868643562554fe568bbf239994bcf1 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 3 Apr 2025 14:11:13 +0200
Subject: [PATCH 088/125] chore: pre-commit cleanup

---
 .../pytorch_text_model_debugging.ipynb        | 100 +++---------------
 1 file changed, 16 insertions(+), 84 deletions(-)

diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
index 8118620..1b1b03c 100644
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
@@ -102,18 +102,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import libraries\n",
     "import torch\n",
@@ -137,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,18 +162,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 81926 \n",
-      "Validation samples: 935\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# For the example, download a random subset of 10% of the original dataset\n",
     "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
@@ -212,17 +194,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vocabulary size: 128257\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_subset = data_subset[\"train\"].with_format(\n",
     "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
@@ -257,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,17 +330,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from neptune_scale import Run\n",
     "from uuid import uuid4\n",
@@ -408,45 +374,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Step 1 / 10241, Loss: 11.765020370483398\n",
-      "Step 2 / 10241, Loss: 11.628341674804688\n",
-      "Step 3 / 10241, Loss: 9.686809539794922\n",
-      "Step 4 / 10241, Loss: 9.657011032104492\n",
-      "Step 5 / 10241, Loss: 9.74715518951416\n",
-      "Step 6 / 10241, Loss: 10.450525283813477\n",
-      "Step 7 / 10241, Loss: 10.163161277770996\n",
-      "Step 8 / 10241, Loss: 10.278069496154785\n",
-      "Step 9 / 10241, Loss: 10.855117797851562\n",
-      "Step 10 / 10241, Loss: 10.982325553894043\n",
-      "Step 11 / 10241, Loss: 10.332056999206543\n",
-      "Step 12 / 10241, Loss: 10.331168174743652\n",
-      "Step 13 / 10241, Loss: 10.291738510131836\n",
-      "Step 14 / 10241, Loss: 9.950544357299805\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[16], line 36\u001b[0m\n\u001b[0;32m     33\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     35\u001b[0m \u001b[38;5;66;03m# Backward pass and optimization\u001b[39;00m\n\u001b[1;32m---> 36\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     37\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     39\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize model and optimizer\n",
     "model = SimpleLLM(\n",
@@ -458,7 +388,9 @@
     ")  # Ignore the buffering index of -100 in the dataset\n",
     "\n",
     "# Define watcher class\n",
-    "watcher = TorchWatcher(model=model, run=run, tensor_stats=[\"mean\", \"norm\"], base_namespace=\"debug_metrics\")\n",
+    "watcher = TorchWatcher(\n",
+    "    model=model, run=run, tensor_stats=[\"mean\", \"norm\"], base_namespace=\"debug_metrics\"\n",
+    ")\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "model.to(device)\n",

From fcf36577e68bac1062cb8bbf1319926231a5045a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 8 Apr 2025 09:41:15 +0200
Subject: [PATCH 089/125] feat: notebook tutorial on debugging training runs
 with Neptune

---
 .../debug_training_runs.ipynb                 | 401 ++++++++++++++++++
 .../debug-model-training-runs/setup.py        | 226 ++++++++++
 .../tutorial-images/debugging_dashboard.png   | Bin 0 -> 151345 bytes
 .../debugging_regex_search.png                | Bin 0 -> 102276 bytes
 .../tutorial-images/debugging_report.png      | Bin 0 -> 124967 bytes
 5 files changed, 627 insertions(+)
 create mode 100644 how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
 create mode 100644 how-to-guides/debug-model-training-runs/setup.py
 create mode 100644 how-to-guides/debug-model-training-runs/tutorial-images/debugging_dashboard.png
 create mode 100644 how-to-guides/debug-model-training-runs/tutorial-images/debugging_regex_search.png
 create mode 100644 how-to-guides/debug-model-training-runs/tutorial-images/debugging_report.png

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
new file mode 100644
index 0000000..337b0ac
--- /dev/null
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Debug Model Training with Neptune\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb%2Fdebugging_model_training/how-to-guides/debug-model-training-runs/debug_trainng_runs.ipynb\"> \n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "Training large models requires careful monitoring of layer-wise metrics to catch issues early. \n",
+    "\n",
+    "Neptune makes it easy to track and visualize metrics like gradient norms across all layers of your model - helping you identify problems like vanishing/exploding gradients quickly.\n",
+    "\n",
+    "In this tutorial, you'll learn how to:\n",
+    "1. **Initialize Neptune** and **log configuration parameters**\n",
+    "2. Track **layer-wise gradient norms** during training \n",
+    "3. Analyze the metrics in Neptune's UI to **debug training issues**\n",
+    "\n",
+    "Step through a pre-configured report [here](https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec) to see a finalized version.\n",
+    "\n",
+    "_Note: This is a code recipe that you can adapt for your own model training needs._\n",
+    "\n",
+    " ![Layer-wise gradient norms visualization in Neptune](tutorial-images/debugging_report.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Before you start\n",
+    "\n",
+    "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
+    "  2. Create a Neptune project for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
+    "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set environment variables\n",
+    "Set your project name and API token as environment variables to log to your Neptune Scale project.\n",
+    "\n",
+    "Uncomment the code block below and replace placeholder values with your own credentials:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set Neptune credentials as environment variables\n",
+    "# %env NEPTUNE_API_TOKEN = YOUR_API_TOKEN\n",
+    "# %env NEPTUNE_PROJECT = WORKSPACE_NAME/PROJECT_NAME"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install dependencies and import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install dependencies\n",
+    "! pip install -qU neptune_scale torch datasets\n",
+    "\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    \"optimizer\": \"Adam\",\n",
+    "    \"batch_size\": 8,\n",
+    "    \"learning_rate\": 0.01,\n",
+    "    \"epochs\": 5,\n",
+    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
+    "    \"input_features\": 256,\n",
+    "    \"embed_size\": 1000,\n",
+    "    \"hidden_size\": 256,  # hidden size for the LSTM\n",
+    "    \"dropout_prob\": 0.3,\n",
+    "    \"num_lstm_layers\": 3,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup data, model and other PyTorch required functions\n",
+    "\n",
+    "The `setup.py` script wraps the data and model creation for use in this tutorial. You can use your own data and model setup if required. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 81926\n",
+      "Validation samples: 935\n",
+      "Vocabulary size: 128257\n",
+      "Model created: MultilayerModel\n",
+      "Optimizer: Adam\n",
+      "Criterion: CrossEntropyLoss\n"
+     ]
+    }
+   ],
+   "source": [
+    "from setup import setup_training\n",
+    "   \n",
+    "# Setup complete training environment\n",
+    "model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(params, use_multilayer=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Debug model training run with Neptune"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: _Initialize Neptune Run object_\n",
+    "\n",
+    "The `Run` object is used to log configuration parameters and metrics. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment\n"
+     ]
+    }
+   ],
+   "source": [
+    "from neptune_scale import Run\n",
+    "\n",
+    "run = Run(\n",
+    "    experiment_name=\"pytorch-text\", # Create a run that is the head of an experiment. This is also used for forking.\n",
+    ")\n",
+    "\n",
+    "print(run.get_experiment_url())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: _Log configuration parameters and tags_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "See configuration parameters:\n",
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment&detailsTab=metadata\n"
+     ]
+    }
+   ],
+   "source": [
+    "run.log_configs(\n",
+    "    {\n",
+    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
+    "        \"config/optimizer\": params[\"optimizer\"],\n",
+    "        \"config/batch_size\": params[\"batch_size\"],\n",
+    "        \"config/epochs\": params[\"epochs\"],\n",
+    "        \"config/num_lstm_layers\": params[\"num_lstm_layers\"],\n",
+    "        \"data/embed_size\": params[\"embed_size\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\", params[\"optimizer\"]])\n",
+    "\n",
+    "print(f\"See configuration parameters:\\n{run.get_experiment_url() + '&detailsTab=metadata'}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: _Execute model training loop_\n",
+    "\n",
+    "In this training loop, we:\n",
+    "1. Register backward hooks to capture gradient norms from all model layers with `register_full_backward_hook()`\n",
+    "2. Track these norms during training to identify potential issues like vanishing/exploding gradients in a dictionary called `debugging_gradient_norms`\n",
+    "3. Log the gradient norms to Neptune for visualization and analysis using the `log_metrics` method\n",
+    "\n",
+    "This approach allows you to monitor the learning dynamics across your entire model architecture in near real-time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "View charts in near real-time:\n",
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment&detailsTab=charts\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-08 09:18:43,794 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[32], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m logits \u001b[38;5;241m=\u001b[39m model(input_ids)\n\u001b[0;32m     28\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m---> 29\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     30\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     32\u001b[0m \u001b[38;5;66;03m# print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\u001b[39;00m\n\u001b[0;32m     33\u001b[0m \n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# Log global training loss\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-08 09:18:49,998 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n",
+      "2025-04-08 09:18:50,001 neptune:ERROR: \n",
+      "\n",
+      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
+      "\n",
+      "This can be caused by either:\n",
+      "- The step of a series value is smaller than the most recently logged step for this series\n",
+      "- the step is exactly the same but the value is different\n",
+      "\n",
+      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Register hooks to track gradients for each layer\n",
+    "def hook_fn(module, grad_input, grad_output):\n",
+    "    layer_name = next(name for name, mod in model.named_modules() if mod is module)\n",
+    "    if grad_input[0] is not None:  # Check if gradients exist\n",
+    "        grad_norm = grad_input[0].norm().item()\n",
+    "        debugging_gradient_norms[f\"debug/gradient/{layer_name}/norm\"] = grad_norm\n",
+    "\n",
+    "# Define dictionary of metrics to log to Neptune\n",
+    "debugging_gradient_norms = {}\n",
+    "# Register hooks once before training\n",
+    "for name, module in model.named_modules():\n",
+    "    module.register_full_backward_hook(hook_fn)\n",
+    "\n",
+    "# Create custom Neptune URLS for tutorial steps\n",
+    "print(f\"View charts in near real-time:\\n{run.get_experiment_url() + '&detailsTab=charts'}\")\n",
+    "\n",
+    "step_counter = 0\n",
+    "# Training loop\n",
+    "for epoch in range(params[\"epochs\"]):\n",
+    "    total_loss = 0\n",
+    "    for batch in train_dataloader:\n",
+    "        model.train()\n",
+    "        step_counter += 1\n",
+    "        input_ids = batch[\"input_ids\"].to(params[\"device\"])\n",
+    "        labels = batch[\"labels\"].to(params[\"device\"])\n",
+    "        optimizer.zero_grad()\n",
+    "        logits = model(input_ids)\n",
+    "        loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Log global training loss and layer-wise gradient norms\n",
+    "        run.log_metrics(\n",
+    "            data={\n",
+    "                  \"metrics/train/loss\": loss.item(), \n",
+    "                  **debugging_gradient_norms\n",
+    "                  },\n",
+    "            step=step_counter,\n",
+    "        )\n",
+    "\n",
+    "# Close run to ensure all operations are processed\n",
+    "run.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4: _Analyze and debug training_\n",
+    "While the model is training, you can start using the Neptune web app to browse your metrics and create custom analyses and visualizations:\n",
+    "1. To visualize the large number of metrics being logged in near real time, navigate to the **Charts** tab of the active run (_or select link above_).\n",
+    "2. Filter the metrics using the [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, enter `gradient & fc & layers.[0-5] & norm` in the search bar. This query filters the metrics for the first 6 layers of the gradients norms of the fully connected layers. You can specify down to exactly the metrics name you want.\n",
+    "\n",
+    "![Alt text](tutorial-images/debugging_regex_search.png)\n",
+    "\n",
+    "3. Export the filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard). The saved dashboard will now only display these metrics during training. This is useful if you know that a certain set of layers can be troublesome during training. \n",
+    "4. Alternatively, use the [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) and create a new chart widget to display all LSTM layers gradient norms in one chart. Again, use the `(.*gradient)(.*lstm)(.*norm)` query. This makes it easy to have an automatically updating chart that allows you to view all layers on a single chart for rapid debugging in case vanishing or exploding gradients appear. \n",
+    "\n",
+    "![Alt text](tutorial-images/debugging_dashboard.png)\n",
+    "\n",
+    "5. To document this behavior, create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics for the model you're training. This allows you to keep track of any anomalies but also to see what worked or did not work during training.\n",
+    "\n",
+    "![Alt text](tutorial-images/debugging_report.png)\n",
+    "\n",
+    "See the pre-configured [example of the training report](https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec).\n",
+    "\n",
+    "See also: PyTorch layer-wise tracking package [here](TODO:Link to integration for tracking layer-wise metrics)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neptune_scale_py_312_base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/how-to-guides/debug-model-training-runs/setup.py b/how-to-guides/debug-model-training-runs/setup.py
new file mode 100644
index 0000000..c66a431
--- /dev/null
+++ b/how-to-guides/debug-model-training-runs/setup.py
@@ -0,0 +1,226 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from datasets import load_dataset
+from typing import Dict, Tuple
+
+# Model Classes
+class SimpleLLM(nn.Module):
+    """A simple language model with a single LSTM layer."""
+    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
+        super(SimpleLLM, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_size)
+        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers=num_layers, batch_first=True)
+        self.fc1 = nn.Linear(hidden_size, vocab_size)
+
+    def forward(self, x):
+        x = self.embedding(x)
+        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple
+        out = self.fc1(lstm_out)  # Use the last output from the LSTM
+        return out
+
+class MultilayerModel(nn.Module):
+    """A larger language model with multiple LSTM and fully connected layers."""
+    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
+        super(MultilayerModel, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_size)
+        
+        # Create multiple LSTM layers
+        self.lstm_layers = nn.ModuleList([
+            nn.LSTM(hidden_size if i > 0 else embed_size, 
+                   hidden_size, 
+                   num_layers=1, 
+                   batch_first=True)
+            for i in range(10)  # 10 LSTM layers
+        ])
+        
+        # Create multiple fully connected layers
+        self.fc_layers = nn.ModuleList([
+            nn.Linear(hidden_size, hidden_size)
+            for _ in range(9)  # 9 FC layers
+        ])
+        
+        # Final layer to project back to vocab size
+        self.final_layer = nn.Linear(hidden_size, vocab_size)
+        
+        # Add dropout for regularization
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, x):
+        # Embedding layer
+        x = self.embedding(x)
+        
+        # Process through LSTM layers
+        for lstm in self.lstm_layers:
+            x, _ = lstm(x)
+            x = self.dropout(x)
+        
+        # Process through FC layers
+        for fc in self.fc_layers:
+            x = fc(x)
+            x = self.dropout(x)
+            x = torch.relu(x)
+        
+        # Final projection
+        out = self.final_layer(x)
+        return out
+
+# Data Loading Function
+def load_and_preprocess_data(params: Dict) -> Tuple[DataLoader, DataLoader, int]:
+    """
+    Load and preprocess the next token prediction dataset from HuggingFace.
+    
+    Args:
+        params (Dict): Dictionary containing parameters for data loading and model configuration
+        
+    Returns:
+        Tuple[DataLoader, DataLoader, int]: Training dataloader, validation dataloader, and vocabulary size
+    """
+    # Download dataset
+    base_url = "https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/"
+    data_files = {
+        "train": base_url + "train-00001-of-00067.parquet",
+        "validation": base_url + "validation-00000-of-00001.parquet",
+    }
+
+    # Load dataset
+    data_subset = load_dataset("parquet", data_files=data_files, num_proc=4)
+    validation_subset = data_subset.get("validation").train_test_split(test_size=0.1)
+
+    print(f"Training samples: {data_subset['train'].num_rows}")
+    print(f"Validation samples: {validation_subset['test'].num_rows}")
+
+    # Convert to PyTorch format
+    train_subset = data_subset["train"].with_format(
+        type="torch", columns=["text", "input_ids", "labels"]
+    )
+    validation_subset = validation_subset["test"].with_format(
+        type="torch", columns=["text", "input_ids", "labels"]
+    )
+
+    # Create dataloaders
+    train_dataloader = DataLoader(train_subset, batch_size=params["batch_size"], shuffle=True)
+    val_dataloader = DataLoader(validation_subset, batch_size=params["batch_size"], shuffle=True)
+
+    # Calculate vocabulary size
+    vocab_size = max([token for sentence in data_subset["train"]["input_ids"] for token in sentence]) + 1
+    print(f"Vocabulary size: {vocab_size}")
+
+    return train_dataloader, val_dataloader, vocab_size
+
+# Helper function to create model
+def create_model(params: Dict, use_multilayer: bool = True) -> nn.Module:
+    """
+    Create a model based on the specified parameters.
+    
+    Args:
+        params (Dict): Dictionary containing model parameters
+        use_multilayer (bool): Whether to use the multilayer model
+        
+    Returns:
+        nn.Module: The created model
+    """
+    if use_multilayer:
+        return MultilayerModel(
+            params["vocab_size"], 
+            params["embed_size"], 
+            params["hidden_size"], 
+            params["num_lstm_layers"]
+        )
+    else:
+        return SimpleLLM(
+            params["vocab_size"], 
+            params["embed_size"], 
+            params["hidden_size"], 
+            params["num_lstm_layers"]
+        )
+
+# Helper function to create optimizer
+def create_optimizer(model: nn.Module, params: Dict) -> optim.Optimizer:
+    """
+    Create an optimizer for the model.
+    
+    Args:
+        model (nn.Module): The model to optimize
+        params (Dict): Dictionary containing optimizer parameters
+        
+    Returns:
+        optim.Optimizer: The created optimizer
+    """
+    if params["optimizer"] == "Adam":
+        return optim.Adam(model.parameters(), lr=params["learning_rate"])
+    elif params["optimizer"] == "SGD":
+        return optim.SGD(model.parameters(), lr=params["learning_rate"])
+    else:
+        raise ValueError(f"Unsupported optimizer: {params['optimizer']}")
+
+# Helper function to create criterion
+def create_criterion() -> nn.Module:
+    """
+    Create a loss function for the model.
+    
+    Returns:
+        nn.Module: The created criterion
+    """
+    return nn.CrossEntropyLoss(ignore_index=-100)  # Ignore the buffering index of -100 in the dataset
+
+# Helper function to setup training
+def setup_training(params: Dict, use_multilayer: bool = True) -> Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]:
+    """
+    Setup the complete training environment.
+    
+    Args:
+        params (Dict): Dictionary containing all parameters
+        use_multilayer (bool): Whether to use the multilayer model
+        
+    Returns:
+        Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]: 
+            Model, optimizer, criterion, train dataloader, validation dataloader, and vocabulary size
+    """
+    # Load data
+    train_dataloader, val_dataloader, vocab_size = load_and_preprocess_data(params)
+    params["vocab_size"] = vocab_size
+    
+    # Create model
+    model = create_model(params, use_multilayer=use_multilayer)
+    # Move model to device
+    model.to(params["device"])
+    print(f"Model created: {model.__class__.__name__}")
+    
+    # Create optimizer
+    optimizer = create_optimizer(model, params)
+    print(f"Optimizer: {optimizer.__class__.__name__}")
+    
+    # Create criterion
+    criterion = create_criterion()
+    print(f"Criterion: {criterion.__class__.__name__}")
+    
+    return model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size
+
+# Explicitly define what should be importable
+__all__ = [
+    'SimpleLLM',
+    'MultilayerModel',
+    'load_and_preprocess_data',
+    'create_model',
+    'create_optimizer',
+    'create_criterion',
+    'setup_training'
+]
+
+if __name__ == "__main__":
+    # Example usage with all required parameters
+    params = {
+        "batch_size": 8,
+        "device": torch.device("cuda" if torch.cuda.is_available() else "cpu"),
+        "embed_size": 1000,
+        "hidden_size": 256,
+        "num_lstm_layers": 3,
+        "optimizer": "Adam",
+        "learning_rate": 0.01,
+        "vocab_size": 50000  # This will be updated by load_and_preprocess_data
+    }
+    
+    # Setup complete training environment
+    model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(params, use_multilayer=True) 
\ No newline at end of file
diff --git a/how-to-guides/debug-model-training-runs/tutorial-images/debugging_dashboard.png b/how-to-guides/debug-model-training-runs/tutorial-images/debugging_dashboard.png
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HcmV?d00001


From 82f4d8b68c6cc735d5108b0fc0d537cf83debe51 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 8 Apr 2025 09:43:06 +0200
Subject: [PATCH 090/125] chore: pre-commit changes

---
 .../debug_training_runs.ipynb                 | 130 +++--------------
 .../debug-model-training-runs/setup.py        | 136 ++++++++++--------
 2 files changed, 97 insertions(+), 169 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 337b0ac..ff1d152 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,27 +121,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 81926\n",
-      "Validation samples: 935\n",
-      "Vocabulary size: 128257\n",
-      "Model created: MultilayerModel\n",
-      "Optimizer: Adam\n",
-      "Criterion: CrossEntropyLoss\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from setup import setup_training\n",
-    "   \n",
+    "\n",
     "# Setup complete training environment\n",
-    "model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(params, use_multilayer=True)"
+    "model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(\n",
+    "    params, use_multilayer=True\n",
+    ")"
    ]
   },
   {
@@ -162,22 +151,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from neptune_scale import Run\n",
     "\n",
     "run = Run(\n",
-    "    experiment_name=\"pytorch-text\", # Create a run that is the head of an experiment. This is also used for forking.\n",
+    "    experiment_name=\"pytorch-text\",  # Create a run that is the head of an experiment. This is also used for forking.\n",
     ")\n",
     "\n",
     "print(run.get_experiment_url())"
@@ -192,18 +173,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "See configuration parameters:\n",
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment&detailsTab=metadata\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "run.log_configs(\n",
     "    {\n",
@@ -239,73 +211,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "View charts in near real-time:\n",
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=pytorch-text&type=experiment&detailsTab=charts\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-04-08 09:18:43,794 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[32], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m logits \u001b[38;5;241m=\u001b[39m model(input_ids)\n\u001b[0;32m     28\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, vocab_size), labels\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m---> 29\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     30\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     32\u001b[0m \u001b[38;5;66;03m# print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\u001b[39;00m\n\u001b[0;32m     33\u001b[0m \n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# Log global training loss\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\_tensor.py:626\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    618\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    619\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    624\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    625\u001b[0m     )\n\u001b[1;32m--> 626\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    628\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\graph.py:823\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    821\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    822\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 823\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    825\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    826\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\autograd\\function.py:292\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBackwardCFunction\u001b[39;00m(_C\u001b[38;5;241m.\u001b[39m_FunctionBase, FunctionCtx, _HookMixin):\n\u001b[0;32m    288\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;124;03m    This class is used for internal autograd work. Do not use.\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 292\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    293\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;124;03m        Apply method used when executing this Node during the backward\u001b[39;00m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;66;03m# _forward_cls is defined by derived class\u001b[39;00m\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# The user should define either backward or vjp but never both.\u001b[39;00m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-04-08 09:18:49,998 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n",
-      "2025-04-08 09:18:50,001 neptune:ERROR: \n",
-      "\n",
-      "NeptuneSeriesStepNonIncreasing: Subsequent steps of a series must be increasing.\n",
-      "\n",
-      "This can be caused by either:\n",
-      "- The step of a series value is smaller than the most recently logged step for this series\n",
-      "- the step is exactly the same but the value is different\n",
-      "\n",
-      "For help, see https://docs-beta.neptune.ai/log_metrics\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Register hooks to track gradients for each layer\n",
     "def hook_fn(module, grad_input, grad_output):\n",
@@ -314,6 +220,7 @@
     "        grad_norm = grad_input[0].norm().item()\n",
     "        debugging_gradient_norms[f\"debug/gradient/{layer_name}/norm\"] = grad_norm\n",
     "\n",
+    "\n",
     "# Define dictionary of metrics to log to Neptune\n",
     "debugging_gradient_norms = {}\n",
     "# Register hooks once before training\n",
@@ -340,10 +247,7 @@
     "\n",
     "        # Log global training loss and layer-wise gradient norms\n",
     "        run.log_metrics(\n",
-    "            data={\n",
-    "                  \"metrics/train/loss\": loss.item(), \n",
-    "                  **debugging_gradient_norms\n",
-    "                  },\n",
+    "            data={\"metrics/train/loss\": loss.item(), **debugging_gradient_norms},\n",
     "            step=step_counter,\n",
     "        )\n",
     "\n",
diff --git a/how-to-guides/debug-model-training-runs/setup.py b/how-to-guides/debug-model-training-runs/setup.py
index c66a431..4607715 100644
--- a/how-to-guides/debug-model-training-runs/setup.py
+++ b/how-to-guides/debug-model-training-runs/setup.py
@@ -1,13 +1,16 @@
+from typing import Dict, Tuple
+
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
 from datasets import load_dataset
-from typing import Dict, Tuple
+from torch.utils.data import DataLoader
+
 
 # Model Classes
 class SimpleLLM(nn.Module):
     """A simple language model with a single LSTM layer."""
+
     def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
         super(SimpleLLM, self).__init__()
         self.embedding = nn.Embedding(vocab_size, embed_size)
@@ -20,65 +23,73 @@ def forward(self, x):
         out = self.fc1(lstm_out)  # Use the last output from the LSTM
         return out
 
+
 class MultilayerModel(nn.Module):
     """A larger language model with multiple LSTM and fully connected layers."""
+
     def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
         super(MultilayerModel, self).__init__()
         self.embedding = nn.Embedding(vocab_size, embed_size)
-        
+
         # Create multiple LSTM layers
-        self.lstm_layers = nn.ModuleList([
-            nn.LSTM(hidden_size if i > 0 else embed_size, 
-                   hidden_size, 
-                   num_layers=1, 
-                   batch_first=True)
-            for i in range(10)  # 10 LSTM layers
-        ])
-        
+        self.lstm_layers = nn.ModuleList(
+            [
+                nn.LSTM(
+                    hidden_size if i > 0 else embed_size,
+                    hidden_size,
+                    num_layers=1,
+                    batch_first=True,
+                )
+                for i in range(10)  # 10 LSTM layers
+            ]
+        )
+
         # Create multiple fully connected layers
-        self.fc_layers = nn.ModuleList([
-            nn.Linear(hidden_size, hidden_size)
-            for _ in range(9)  # 9 FC layers
-        ])
-        
+        self.fc_layers = nn.ModuleList(
+            [nn.Linear(hidden_size, hidden_size) for _ in range(9)]  # 9 FC layers
+        )
+
         # Final layer to project back to vocab size
         self.final_layer = nn.Linear(hidden_size, vocab_size)
-        
+
         # Add dropout for regularization
         self.dropout = nn.Dropout(0.1)
 
     def forward(self, x):
         # Embedding layer
         x = self.embedding(x)
-        
+
         # Process through LSTM layers
         for lstm in self.lstm_layers:
             x, _ = lstm(x)
             x = self.dropout(x)
-        
+
         # Process through FC layers
         for fc in self.fc_layers:
             x = fc(x)
             x = self.dropout(x)
             x = torch.relu(x)
-        
+
         # Final projection
         out = self.final_layer(x)
         return out
 
+
 # Data Loading Function
 def load_and_preprocess_data(params: Dict) -> Tuple[DataLoader, DataLoader, int]:
     """
     Load and preprocess the next token prediction dataset from HuggingFace.
-    
+
     Args:
         params (Dict): Dictionary containing parameters for data loading and model configuration
-        
+
     Returns:
         Tuple[DataLoader, DataLoader, int]: Training dataloader, validation dataloader, and vocabulary size
     """
     # Download dataset
-    base_url = "https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/"
+    base_url = (
+        "https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/"
+    )
     data_files = {
         "train": base_url + "train-00001-of-00067.parquet",
         "validation": base_url + "validation-00000-of-00001.parquet",
@@ -104,47 +115,51 @@ def load_and_preprocess_data(params: Dict) -> Tuple[DataLoader, DataLoader, int]
     val_dataloader = DataLoader(validation_subset, batch_size=params["batch_size"], shuffle=True)
 
     # Calculate vocabulary size
-    vocab_size = max([token for sentence in data_subset["train"]["input_ids"] for token in sentence]) + 1
+    vocab_size = (
+        max([token for sentence in data_subset["train"]["input_ids"] for token in sentence]) + 1
+    )
     print(f"Vocabulary size: {vocab_size}")
 
     return train_dataloader, val_dataloader, vocab_size
 
+
 # Helper function to create model
 def create_model(params: Dict, use_multilayer: bool = True) -> nn.Module:
     """
     Create a model based on the specified parameters.
-    
+
     Args:
         params (Dict): Dictionary containing model parameters
         use_multilayer (bool): Whether to use the multilayer model
-        
+
     Returns:
         nn.Module: The created model
     """
     if use_multilayer:
         return MultilayerModel(
-            params["vocab_size"], 
-            params["embed_size"], 
-            params["hidden_size"], 
-            params["num_lstm_layers"]
+            params["vocab_size"],
+            params["embed_size"],
+            params["hidden_size"],
+            params["num_lstm_layers"],
         )
     else:
         return SimpleLLM(
-            params["vocab_size"], 
-            params["embed_size"], 
-            params["hidden_size"], 
-            params["num_lstm_layers"]
+            params["vocab_size"],
+            params["embed_size"],
+            params["hidden_size"],
+            params["num_lstm_layers"],
         )
 
+
 # Helper function to create optimizer
 def create_optimizer(model: nn.Module, params: Dict) -> optim.Optimizer:
     """
     Create an optimizer for the model.
-    
+
     Args:
         model (nn.Module): The model to optimize
         params (Dict): Dictionary containing optimizer parameters
-        
+
     Returns:
         optim.Optimizer: The created optimizer
     """
@@ -155,58 +170,65 @@ def create_optimizer(model: nn.Module, params: Dict) -> optim.Optimizer:
     else:
         raise ValueError(f"Unsupported optimizer: {params['optimizer']}")
 
+
 # Helper function to create criterion
 def create_criterion() -> nn.Module:
     """
     Create a loss function for the model.
-    
+
     Returns:
         nn.Module: The created criterion
     """
-    return nn.CrossEntropyLoss(ignore_index=-100)  # Ignore the buffering index of -100 in the dataset
+    return nn.CrossEntropyLoss(
+        ignore_index=-100
+    )  # Ignore the buffering index of -100 in the dataset
+
 
 # Helper function to setup training
-def setup_training(params: Dict, use_multilayer: bool = True) -> Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]:
+def setup_training(
+    params: Dict, use_multilayer: bool = True
+) -> Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]:
     """
     Setup the complete training environment.
-    
+
     Args:
         params (Dict): Dictionary containing all parameters
         use_multilayer (bool): Whether to use the multilayer model
-        
+
     Returns:
-        Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]: 
+        Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]:
             Model, optimizer, criterion, train dataloader, validation dataloader, and vocabulary size
     """
     # Load data
     train_dataloader, val_dataloader, vocab_size = load_and_preprocess_data(params)
     params["vocab_size"] = vocab_size
-    
+
     # Create model
     model = create_model(params, use_multilayer=use_multilayer)
     # Move model to device
     model.to(params["device"])
     print(f"Model created: {model.__class__.__name__}")
-    
+
     # Create optimizer
     optimizer = create_optimizer(model, params)
     print(f"Optimizer: {optimizer.__class__.__name__}")
-    
+
     # Create criterion
     criterion = create_criterion()
     print(f"Criterion: {criterion.__class__.__name__}")
-    
+
     return model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size
 
+
 # Explicitly define what should be importable
 __all__ = [
-    'SimpleLLM',
-    'MultilayerModel',
-    'load_and_preprocess_data',
-    'create_model',
-    'create_optimizer',
-    'create_criterion',
-    'setup_training'
+    "SimpleLLM",
+    "MultilayerModel",
+    "load_and_preprocess_data",
+    "create_model",
+    "create_optimizer",
+    "create_criterion",
+    "setup_training",
 ]
 
 if __name__ == "__main__":
@@ -219,8 +241,10 @@ def setup_training(params: Dict, use_multilayer: bool = True) -> Tuple[nn.Module
         "num_lstm_layers": 3,
         "optimizer": "Adam",
         "learning_rate": 0.01,
-        "vocab_size": 50000  # This will be updated by load_and_preprocess_data
+        "vocab_size": 50000,  # This will be updated by load_and_preprocess_data
     }
-    
+
     # Setup complete training environment
-    model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(params, use_multilayer=True) 
\ No newline at end of file
+    model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(
+        params, use_multilayer=True
+    )

From 8c562965233e3b17afebb3b9e0aa66acecae2126 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 8 Apr 2025 09:48:13 +0200
Subject: [PATCH 091/125] chore: update colab link for branch

---
 .../debug-model-training-runs/debug_training_runs.ipynb         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index ff1d152..7d18660 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Debug Model Training with Neptune\n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb%2Fdebugging_model_training/how-to-guides/debug-model-training-runs/debug_trainng_runs.ipynb\"> \n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
     "</a>"
    ]

From 247679a1dbedff4ca7decb97495da43fb74e4d27 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 8 Apr 2025 11:55:31 +0200
Subject: [PATCH 092/125] refactor: keep notebook more self-contained for use
 in Colab

---
 .../debug_training_runs.ipynb                 | 120 ++++++++++++++++--
 1 file changed, 112 insertions(+), 8 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 7d18660..49ca6b9 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -114,9 +114,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setup data, model and other PyTorch required functions\n",
+    "### Setup data, model and other dependencies\n",
     "\n",
-    "The `setup.py` script wraps the data and model creation for use in this tutorial. You can use your own data and model setup if required. "
+    "In the next cell, we'll:\n",
+    "1. Import required PyTorch and HuggingFace libraries\n",
+    "2. Download and load a [next token prediction dataset](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset) from HuggingFace\n",
+    "3. Create PyTorch DataLoaders for training\n",
+    "4. Calculate vocabulary size for the model\n",
+    "5. Define a multilayer model for training\n",
+    "\n",
+    "You can modify this setup to use your own data and model architecture if needed."
    ]
   },
   {
@@ -125,12 +132,109 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from setup import setup_training\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from datasets import load_dataset\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "# Download dataset\n",
+    "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
+    "data_files = {\n",
+    "    \"train\": base_url + \"train-00001-of-00067.parquet\",\n",
+    "    \"validation\": base_url + \"validation-00000-of-00001.parquet\",\n",
+    "}\n",
+    "\n",
+    "# Load dataset\n",
+    "data_subset = load_dataset(\"parquet\", data_files=data_files, num_proc=4)\n",
+    "# validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
+    "\n",
+    "print(f\"Training samples: {data_subset['train'].num_rows}\")\n",
+    "# print(f\"Validation samples: {validation_subset['test'].num_rows}\")\n",
+    "\n",
+    "# Convert to PyTorch format\n",
+    "train_subset = data_subset[\"train\"].with_format(\n",
+    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
+    ")\n",
+    "\"\"\"validation_subset = validation_subset[\"test\"].with_format(\n",
+    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
+    ")\"\"\"\n",
+    "\n",
+    "# Create dataloaders\n",
+    "train_dataloader = DataLoader(train_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
+    "# val_dataloader = DataLoader(validation_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
+    "\n",
+    "# Calculate vocabulary size\n",
+    "params[\"vocab_size\"] = (\n",
+    "    max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
+    ")\n",
+    "print(f\"Vocabulary size: {params[\"vocab_size\"]}\")\n",
+    "\n",
+    "\n",
+    "# Create model\n",
+    "class MultilayerModel(nn.Module):\n",
+    "    \"\"\"A larger language model with multiple LSTM and fully connected layers.\"\"\"\n",
+    "\n",
+    "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
+    "        super(MultilayerModel, self).__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
     "\n",
-    "# Setup complete training environment\n",
-    "model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(\n",
-    "    params, use_multilayer=True\n",
-    ")"
+    "        # Create multiple LSTM layers\n",
+    "        self.lstm_layers = nn.ModuleList(\n",
+    "            [\n",
+    "                nn.LSTM(\n",
+    "                    hidden_size if i > 0 else embed_size,\n",
+    "                    hidden_size,\n",
+    "                    num_layers=1,\n",
+    "                    batch_first=True,\n",
+    "                )\n",
+    "                for i in range(10)  # 10 LSTM layers\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
+    "        # Create multiple fully connected layers\n",
+    "        self.fc_layers = nn.ModuleList(\n",
+    "            [nn.Linear(hidden_size, hidden_size) for _ in range(9)]  # 9 FC layers\n",
+    "        )\n",
+    "\n",
+    "        # Final layer to project back to vocab size\n",
+    "        self.final_layer = nn.Linear(hidden_size, vocab_size)\n",
+    "\n",
+    "        # Add dropout for regularization\n",
+    "        self.dropout = nn.Dropout(0.1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Embedding layer\n",
+    "        x = self.embedding(x)\n",
+    "\n",
+    "        # Process through LSTM layers\n",
+    "        for lstm in self.lstm_layers:\n",
+    "            x, _ = lstm(x)\n",
+    "            x = self.dropout(x)\n",
+    "\n",
+    "        # Process through FC layers\n",
+    "        for fc in self.fc_layers:\n",
+    "            x = fc(x)\n",
+    "            x = self.dropout(x)\n",
+    "            x = torch.relu(x)\n",
+    "\n",
+    "        # Final projection\n",
+    "        out = self.final_layer(x)\n",
+    "        return out\n",
+    "\n",
+    "\n",
+    "model = MultilayerModel(\n",
+    "    params[\"vocab_size\"],\n",
+    "    params[\"embed_size\"],\n",
+    "    params[\"hidden_size\"],\n",
+    ")\n",
+    "print(f\"Model created: {model.__class__.__name__}\")\n",
+    "optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
+    "print(f\"Optimizer: {optimizer.__class__.__name__}\")\n",
+    "criterion = nn.CrossEntropyLoss(\n",
+    "    ignore_index=-100\n",
+    ")  # Ignore the buffering index of -100 in the dataset\n",
+    "print(f\"Criterion: {criterion.__class__.__name__}\")"
    ]
   },
   {
@@ -241,7 +345,7 @@
     "        labels = batch[\"labels\"].to(params[\"device\"])\n",
     "        optimizer.zero_grad()\n",
     "        logits = model(input_ids)\n",
-    "        loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
+    "        loss = criterion(logits.view(-1, params[\"vocab_size\"]), labels.view(-1))\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
     "\n",

From 408ba02dff58b1c5ef048af5ba0951e5c27fd5a6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 8 Apr 2025 12:00:42 +0200
Subject: [PATCH 093/125] fix: f string compatibility in Colab

---
 .../debug_training_runs.ipynb                  | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 49ca6b9..48edb9d 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -128,9 +128,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 81926\n",
+      "Vocabulary size: 128257\n",
+      "Model created: MultilayerModel\n",
+      "Optimizer: Adam\n",
+      "Criterion: CrossEntropyLoss\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -168,7 +180,7 @@
     "params[\"vocab_size\"] = (\n",
     "    max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
     ")\n",
-    "print(f\"Vocabulary size: {params[\"vocab_size\"]}\")\n",
+    "print(f\"Vocabulary size: {params['vocab_size']}\")\n",
     "\n",
     "\n",
     "# Create model\n",

From 314e8a2ede60c6baa20cc30806a400d4ca33f627 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 8 Apr 2025 12:03:22 +0200
Subject: [PATCH 094/125] fix: ensure model object is on correct device

---
 .../debug-model-training-runs/debug_training_runs.ipynb          | 1 +
 1 file changed, 1 insertion(+)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 48edb9d..e101a59 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -240,6 +240,7 @@
     "    params[\"embed_size\"],\n",
     "    params[\"hidden_size\"],\n",
     ")\n",
+    "model.to(params[\"device\"])\n",
     "print(f\"Model created: {model.__class__.__name__}\")\n",
     "optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
     "print(f\"Optimizer: {optimizer.__class__.__name__}\")\n",

From 62227bc6985a733aea940b7c60b8aad8f519b552 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 9 Apr 2025 10:13:00 +0200
Subject: [PATCH 095/125] chore: Add header links to GH, Neptune and docs

These need to be updated the the final branch when merged
---
 .../debug-model-training-runs/debug_training_runs.ipynb  | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index e101a59..4c76655 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -8,6 +8,15 @@
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
+    "</a>\n",
+    "<a target=\"_blank\" href=\"https://github.com/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\">\n",
+    "  <img alt=\"Open in GitHub\" src=\"https://img.shields.io/badge/Open_in_GitHub-blue?logo=github&labelColor=black\">\n",
+    "</a>\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
+    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
+    "</a>\n",
+    "<a target=\"_blank\" href=\"https://docs-beta.neptune.ai/tutorials/\">\n",
+    "  <img alt=\"View tutorial in docs\" src=\"https://neptune.ai/wp-content/uploads/2024/01/docs-badge-2.svg\">\n",
     "</a>"
    ]
   },

From 505f41c5bae3e4dd35ebc2aebcd1233156f14a6f Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 14 Apr 2025 11:26:55 +0200
Subject: [PATCH 096/125] style: minor updates to markdown

---
 .../debug_training_runs.ipynb                     | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 4c76655..b2d5a11 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -12,9 +12,6 @@
     "<a target=\"_blank\" href=\"https://github.com/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\">\n",
     "  <img alt=\"Open in GitHub\" src=\"https://img.shields.io/badge/Open_in_GitHub-blue?logo=github&labelColor=black\">\n",
     "</a>\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
-    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
-    "</a>\n",
     "<a target=\"_blank\" href=\"https://docs-beta.neptune.ai/tutorials/\">\n",
     "  <img alt=\"View tutorial in docs\" src=\"https://neptune.ai/wp-content/uploads/2024/01/docs-badge-2.svg\">\n",
     "</a>"
@@ -34,7 +31,10 @@
     "2. Track **layer-wise gradient norms** during training \n",
     "3. Analyze the metrics in Neptune's UI to **debug training issues**\n",
     "\n",
-    "Step through a pre-configured report [here](https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec) to see a finalized version.\n",
+    "Step through a pre-configured report:\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
+    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
+    "</a>\n",
     "\n",
     "_Note: This is a code recipe that you can adapt for your own model training needs._\n",
     "\n",
@@ -132,7 +132,7 @@
     "4. Calculate vocabulary size for the model\n",
     "5. Define a multilayer model for training\n",
     "\n",
-    "You can modify this setup to use your own data and model architecture if needed."
+    "_You can modify this setup to use your own data and model architecture._"
    ]
   },
   {
@@ -191,7 +191,6 @@
     ")\n",
     "print(f\"Vocabulary size: {params['vocab_size']}\")\n",
     "\n",
-    "\n",
     "# Create model\n",
     "class MultilayerModel(nn.Module):\n",
     "    \"\"\"A larger language model with multiple LSTM and fully connected layers.\"\"\"\n",
@@ -284,7 +283,7 @@
     "from neptune_scale import Run\n",
     "\n",
     "run = Run(\n",
-    "    experiment_name=\"pytorch-text\",  # Create a run that is the head of an experiment. This is also used for forking.\n",
+    "    experiment_name=\"pytorch-text\",  # Create a run that is the head of an experiment.\n",
     ")\n",
     "\n",
     "print(run.get_experiment_url())"
@@ -314,7 +313,7 @@
     "    }\n",
     ")\n",
     "\n",
-    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\", params[\"optimizer\"]])\n",
+    "run.add_tags(tags=[\"text\", \"LLM\", \"multi-layer\", params[\"optimizer\"]])\n",
     "\n",
     "print(f\"See configuration parameters:\\n{run.get_experiment_url() + '&detailsTab=metadata'}\")"
    ]

From bed489922715d058545a94357dfba23364b5ebae Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 14 Apr 2025 11:38:03 +0200
Subject: [PATCH 097/125] chore: remove unused code

---
 .../debug-model-training-runs/setup.py        | 250 ------------------
 1 file changed, 250 deletions(-)
 delete mode 100644 how-to-guides/debug-model-training-runs/setup.py

diff --git a/how-to-guides/debug-model-training-runs/setup.py b/how-to-guides/debug-model-training-runs/setup.py
deleted file mode 100644
index 4607715..0000000
--- a/how-to-guides/debug-model-training-runs/setup.py
+++ /dev/null
@@ -1,250 +0,0 @@
-from typing import Dict, Tuple
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-
-
-# Model Classes
-class SimpleLLM(nn.Module):
-    """A simple language model with a single LSTM layer."""
-
-    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
-        super(SimpleLLM, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, embed_size)
-        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers=num_layers, batch_first=True)
-        self.fc1 = nn.Linear(hidden_size, vocab_size)
-
-    def forward(self, x):
-        x = self.embedding(x)
-        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple
-        out = self.fc1(lstm_out)  # Use the last output from the LSTM
-        return out
-
-
-class MultilayerModel(nn.Module):
-    """A larger language model with multiple LSTM and fully connected layers."""
-
-    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
-        super(MultilayerModel, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, embed_size)
-
-        # Create multiple LSTM layers
-        self.lstm_layers = nn.ModuleList(
-            [
-                nn.LSTM(
-                    hidden_size if i > 0 else embed_size,
-                    hidden_size,
-                    num_layers=1,
-                    batch_first=True,
-                )
-                for i in range(10)  # 10 LSTM layers
-            ]
-        )
-
-        # Create multiple fully connected layers
-        self.fc_layers = nn.ModuleList(
-            [nn.Linear(hidden_size, hidden_size) for _ in range(9)]  # 9 FC layers
-        )
-
-        # Final layer to project back to vocab size
-        self.final_layer = nn.Linear(hidden_size, vocab_size)
-
-        # Add dropout for regularization
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x):
-        # Embedding layer
-        x = self.embedding(x)
-
-        # Process through LSTM layers
-        for lstm in self.lstm_layers:
-            x, _ = lstm(x)
-            x = self.dropout(x)
-
-        # Process through FC layers
-        for fc in self.fc_layers:
-            x = fc(x)
-            x = self.dropout(x)
-            x = torch.relu(x)
-
-        # Final projection
-        out = self.final_layer(x)
-        return out
-
-
-# Data Loading Function
-def load_and_preprocess_data(params: Dict) -> Tuple[DataLoader, DataLoader, int]:
-    """
-    Load and preprocess the next token prediction dataset from HuggingFace.
-
-    Args:
-        params (Dict): Dictionary containing parameters for data loading and model configuration
-
-    Returns:
-        Tuple[DataLoader, DataLoader, int]: Training dataloader, validation dataloader, and vocabulary size
-    """
-    # Download dataset
-    base_url = (
-        "https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/"
-    )
-    data_files = {
-        "train": base_url + "train-00001-of-00067.parquet",
-        "validation": base_url + "validation-00000-of-00001.parquet",
-    }
-
-    # Load dataset
-    data_subset = load_dataset("parquet", data_files=data_files, num_proc=4)
-    validation_subset = data_subset.get("validation").train_test_split(test_size=0.1)
-
-    print(f"Training samples: {data_subset['train'].num_rows}")
-    print(f"Validation samples: {validation_subset['test'].num_rows}")
-
-    # Convert to PyTorch format
-    train_subset = data_subset["train"].with_format(
-        type="torch", columns=["text", "input_ids", "labels"]
-    )
-    validation_subset = validation_subset["test"].with_format(
-        type="torch", columns=["text", "input_ids", "labels"]
-    )
-
-    # Create dataloaders
-    train_dataloader = DataLoader(train_subset, batch_size=params["batch_size"], shuffle=True)
-    val_dataloader = DataLoader(validation_subset, batch_size=params["batch_size"], shuffle=True)
-
-    # Calculate vocabulary size
-    vocab_size = (
-        max([token for sentence in data_subset["train"]["input_ids"] for token in sentence]) + 1
-    )
-    print(f"Vocabulary size: {vocab_size}")
-
-    return train_dataloader, val_dataloader, vocab_size
-
-
-# Helper function to create model
-def create_model(params: Dict, use_multilayer: bool = True) -> nn.Module:
-    """
-    Create a model based on the specified parameters.
-
-    Args:
-        params (Dict): Dictionary containing model parameters
-        use_multilayer (bool): Whether to use the multilayer model
-
-    Returns:
-        nn.Module: The created model
-    """
-    if use_multilayer:
-        return MultilayerModel(
-            params["vocab_size"],
-            params["embed_size"],
-            params["hidden_size"],
-            params["num_lstm_layers"],
-        )
-    else:
-        return SimpleLLM(
-            params["vocab_size"],
-            params["embed_size"],
-            params["hidden_size"],
-            params["num_lstm_layers"],
-        )
-
-
-# Helper function to create optimizer
-def create_optimizer(model: nn.Module, params: Dict) -> optim.Optimizer:
-    """
-    Create an optimizer for the model.
-
-    Args:
-        model (nn.Module): The model to optimize
-        params (Dict): Dictionary containing optimizer parameters
-
-    Returns:
-        optim.Optimizer: The created optimizer
-    """
-    if params["optimizer"] == "Adam":
-        return optim.Adam(model.parameters(), lr=params["learning_rate"])
-    elif params["optimizer"] == "SGD":
-        return optim.SGD(model.parameters(), lr=params["learning_rate"])
-    else:
-        raise ValueError(f"Unsupported optimizer: {params['optimizer']}")
-
-
-# Helper function to create criterion
-def create_criterion() -> nn.Module:
-    """
-    Create a loss function for the model.
-
-    Returns:
-        nn.Module: The created criterion
-    """
-    return nn.CrossEntropyLoss(
-        ignore_index=-100
-    )  # Ignore the buffering index of -100 in the dataset
-
-
-# Helper function to setup training
-def setup_training(
-    params: Dict, use_multilayer: bool = True
-) -> Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]:
-    """
-    Setup the complete training environment.
-
-    Args:
-        params (Dict): Dictionary containing all parameters
-        use_multilayer (bool): Whether to use the multilayer model
-
-    Returns:
-        Tuple[nn.Module, optim.Optimizer, nn.Module, DataLoader, DataLoader, int]:
-            Model, optimizer, criterion, train dataloader, validation dataloader, and vocabulary size
-    """
-    # Load data
-    train_dataloader, val_dataloader, vocab_size = load_and_preprocess_data(params)
-    params["vocab_size"] = vocab_size
-
-    # Create model
-    model = create_model(params, use_multilayer=use_multilayer)
-    # Move model to device
-    model.to(params["device"])
-    print(f"Model created: {model.__class__.__name__}")
-
-    # Create optimizer
-    optimizer = create_optimizer(model, params)
-    print(f"Optimizer: {optimizer.__class__.__name__}")
-
-    # Create criterion
-    criterion = create_criterion()
-    print(f"Criterion: {criterion.__class__.__name__}")
-
-    return model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size
-
-
-# Explicitly define what should be importable
-__all__ = [
-    "SimpleLLM",
-    "MultilayerModel",
-    "load_and_preprocess_data",
-    "create_model",
-    "create_optimizer",
-    "create_criterion",
-    "setup_training",
-]
-
-if __name__ == "__main__":
-    # Example usage with all required parameters
-    params = {
-        "batch_size": 8,
-        "device": torch.device("cuda" if torch.cuda.is_available() else "cpu"),
-        "embed_size": 1000,
-        "hidden_size": 256,
-        "num_lstm_layers": 3,
-        "optimizer": "Adam",
-        "learning_rate": 0.01,
-        "vocab_size": 50000,  # This will be updated by load_and_preprocess_data
-    }
-
-    # Setup complete training environment
-    model, optimizer, criterion, train_dataloader, val_dataloader, vocab_size = setup_training(
-        params, use_multilayer=True
-    )

From 75885bbf9499890992246f23a6278c02e57f039a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 15 Apr 2025 13:42:14 +0200
Subject: [PATCH 098/125] style: remove image reference

---
 .../debug_training_runs.ipynb                 |  21 ++++++++++--------
 .../tutorial-images/debugging_dashboard.png   | Bin 151345 -> 0 bytes
 .../debugging_regex_search.png                | Bin 102276 -> 0 bytes
 .../tutorial-images/debugging_report.png      | Bin 124967 -> 0 bytes
 4 files changed, 12 insertions(+), 9 deletions(-)
 delete mode 100644 how-to-guides/debug-model-training-runs/tutorial-images/debugging_dashboard.png
 delete mode 100644 how-to-guides/debug-model-training-runs/tutorial-images/debugging_regex_search.png
 delete mode 100644 how-to-guides/debug-model-training-runs/tutorial-images/debugging_report.png

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index b2d5a11..8728019 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -36,9 +36,7 @@
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
-    "_Note: This is a code recipe that you can adapt for your own model training needs._\n",
-    "\n",
-    " ![Layer-wise gradient norms visualization in Neptune](tutorial-images/debugging_report.png)"
+    "_Note: This is a code recipe that you can adapt for your own model training needs._"
    ]
   },
   {
@@ -387,20 +385,25 @@
     "### Step 4: _Analyze and debug training_\n",
     "While the model is training, you can start using the Neptune web app to browse your metrics and create custom analyses and visualizations:\n",
     "1. To visualize the large number of metrics being logged in near real time, navigate to the **Charts** tab of the active run (_or select link above_).\n",
-    "2. Filter the metrics using the [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, enter `gradient & fc & layers.[0-5] & norm` in the search bar. This query filters the metrics for the first 6 layers of the gradients norms of the fully connected layers. You can specify down to exactly the metrics name you want.\n",
     "\n",
-    "![Alt text](tutorial-images/debugging_regex_search.png)\n",
+    "2. Filter the metrics using the [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, enter `gradient & fc & layers.[0-5] & norm` in the search bar. This query filters the metrics for the first 6 layers of the gradients norms of the fully connected layers. You can specify down to exactly the metrics name you want.\n",
     "\n",
     "3. Export the filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard). The saved dashboard will now only display these metrics during training. This is useful if you know that a certain set of layers can be troublesome during training. \n",
+    "\n",
     "4. Alternatively, use the [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) and create a new chart widget to display all LSTM layers gradient norms in one chart. Again, use the `(.*gradient)(.*lstm)(.*norm)` query. This makes it easy to have an automatically updating chart that allows you to view all layers on a single chart for rapid debugging in case vanishing or exploding gradients appear. \n",
     "\n",
-    "![Alt text](tutorial-images/debugging_dashboard.png)\n",
+    "**Explore pre-configured dashboard:**\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=9e5a20fe-0810-4374-809c-02f55afe0548&dash=dashboard&dashboardId=9e6a2b2a-bc54-4eca-9c1b-3b151c3d53d9&compare=uJI-OW3tOzqHtPU9F8K_zIw4wtBWp0vj5uPHtuoXDVbA\">\n",
+    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
+    "</a>\n",
     "\n",
-    "5. To document this behavior, create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics for the model you're training. This allows you to keep track of any anomalies but also to see what worked or did not work during training.\n",
     "\n",
-    "![Alt text](tutorial-images/debugging_report.png)\n",
+    "5. To document this behavior, create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics for the model you're training. This allows you to keep track of any anomalies but also to see what worked or did not work during training.\n",
     "\n",
-    "See the pre-configured [example of the training report](https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec).\n",
+    "**Explore pre-configured report:**\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
+    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
+    "</a>\n",
     "\n",
     "See also: PyTorch layer-wise tracking package [here](TODO:Link to integration for tracking layer-wise metrics)\n"
    ]
diff --git a/how-to-guides/debug-model-training-runs/tutorial-images/debugging_dashboard.png b/how-to-guides/debug-model-training-runs/tutorial-images/debugging_dashboard.png
deleted file mode 100644
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From 33d3305445b59577b3b70ea6bab48f73f1af5a64 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 15 Apr 2025 17:10:14 +0200
Subject: [PATCH 099/125] style: update ending steps and add button links to
 examples

---
 .../debug_training_runs.ipynb                 | 54 ++++++++++++++-----
 1 file changed, 40 insertions(+), 14 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 8728019..7759514 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -382,30 +382,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 4: _Analyze and debug training_\n",
-    "While the model is training, you can start using the Neptune web app to browse your metrics and create custom analyses and visualizations:\n",
-    "1. To visualize the large number of metrics being logged in near real time, navigate to the **Charts** tab of the active run (_or select link above_).\n",
-    "\n",
-    "2. Filter the metrics using the [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, enter `gradient & fc & layers.[0-5] & norm` in the search bar. This query filters the metrics for the first 6 layers of the gradients norms of the fully connected layers. You can specify down to exactly the metrics name you want.\n",
-    "\n",
-    "3. Export the filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard). The saved dashboard will now only display these metrics during training. This is useful if you know that a certain set of layers can be troublesome during training. \n",
+    "### Step 4: Analyze and Debug Training\n",
+    "While your model trains, use Neptune's web interface to monitor and analyze metrics in near real-time:\n",
+    "\n",
+    "**1. Real-time Metric Visualization**\n",
+    "- Navigate to the _Charts_ tab to view live training metrics\n",
+    "- Monitor multiple metrics simultaneously\n",
+    "- Track training progress in real-time\n",
+    "\n",
+    "**2. Advanced Metric Filtering**\n",
+    "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/charts#filtering-charts)\n",
+    "- Example: `gradient & fc & layers.[0-5] & norm` filters gradients for the first 6 fully connected layers\n",
+    "- Perfect for isolating problematic layers or components\n",
+    "\n",
+    "**3. Create Custom Dashboards**\n",
+    "- Save filtered metrics to a [custom dashboard](https://docs.neptune.ai/custom_dashboard) for continuous monitoring\n",
+    "- Automatically updates during training\n",
+    "- Share with team members\n",
+    "- Ideal for tracking known problematic layers\n",
+    "\n",
+    "_Example dashboard:_\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=9e5a20fe-0810-4374-809c-02f55afe0548&dash=dashboard&dashboardId=9e43838c-2d76-4be0-a956-d8e6ad89ae23&compare=uJI-OW3tOzqHtPU9F8K_zIw4wtBWp0vj5uPHtuoXDVbA\">\n",
+    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
+    "</a>\n",
     "\n",
-    "4. Alternatively, use the [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) and create a new chart widget to display all LSTM layers gradient norms in one chart. Again, use the `(.*gradient)(.*lstm)(.*norm)` query. This makes it easy to have an automatically updating chart that allows you to view all layers on a single chart for rapid debugging in case vanishing or exploding gradients appear. \n",
+    "**4. Dynamic Metric Analysis**\n",
+    "- Group related metrics (e.g., all LSTM layer gradients)\n",
+    "- Create automatically updating charts by [selecting metrics dynamically](https://docs.neptune.ai/chart_widget/#dynamic-metric-selection)\n",
+    "- Quickly identify vanishing/exploding gradients\n",
+    "- Example query: `(.*gradient)(.*lstm)(.*norm)`\n",
     "\n",
-    "**Explore pre-configured dashboard:**\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=9e5a20fe-0810-4374-809c-02f55afe0548&dash=dashboard&dashboardId=9e6a2b2a-bc54-4eca-9c1b-3b151c3d53d9&compare=uJI-OW3tOzqHtPU9F8K_zIw4wtBWp0vj5uPHtuoXDVbA\">\n",
+    "_Example dashboard:_\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=9e5a20fe-0810-4374-809c-02f55afe0548&dash=dashboard&dashboardId=9e43838c-2d76-4be0-a956-d8e6ad89ae23&compare=uJI-OW3tOzqHtPU9F8K_zIw4wtBWp0vj5uPHtuoXDVbA\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
+    "#### 5. Document Training Insights\n",
+    "- Create [custom reports](https://docs.neptune.ai/reports) to track training progress\n",
+    "- Document anomalies and successful configurations\n",
+    "- Maintain training history\n",
+    "- Share insights with team members\n",
     "\n",
-    "5. To document this behavior, create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics for the model you're training. This allows you to keep track of any anomalies but also to see what worked or did not work during training.\n",
-    "\n",
-    "**Explore pre-configured report:**\n",
+    "_Example report:_\n",
     "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
-    "See also: PyTorch layer-wise tracking package [here](TODO:Link to integration for tracking layer-wise metrics)\n"
+    "---\n",
+    "\n",
+    "**Additional Resources:**\n",
+    "- [PyTorch Layer-wise Tracking Package](TODO:Link to integration for tracking layer-wise metrics)\n"
    ]
   }
  ],

From 494379f350cfef2b500e658c58558e72ad91f239 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 16 Apr 2025 12:21:34 +0200
Subject: [PATCH 100/125] style: update text for better readability

---
 .../debug_training_runs.ipynb                      | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 7759514..0509f9f 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -382,20 +382,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 4: Analyze and Debug Training\n",
+    "### Step 4: Analyze and debug training\n",
     "While your model trains, use Neptune's web interface to monitor and analyze metrics in near real-time:\n",
     "\n",
-    "**1. Real-time Metric Visualization**\n",
+    "**1. Real-time metric visualization**\n",
     "- Navigate to the _Charts_ tab to view live training metrics\n",
     "- Monitor multiple metrics simultaneously\n",
     "- Track training progress in real-time\n",
     "\n",
-    "**2. Advanced Metric Filtering**\n",
+    "**2. Advanced metric filtering**\n",
     "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/charts#filtering-charts)\n",
     "- Example: `gradient & fc & layers.[0-5] & norm` filters gradients for the first 6 fully connected layers\n",
     "- Perfect for isolating problematic layers or components\n",
     "\n",
-    "**3. Create Custom Dashboards**\n",
+    "**3. Create custom dashboards**\n",
     "- Save filtered metrics to a [custom dashboard](https://docs.neptune.ai/custom_dashboard) for continuous monitoring\n",
     "- Automatically updates during training\n",
     "- Share with team members\n",
@@ -406,7 +406,7 @@
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
-    "**4. Dynamic Metric Analysis**\n",
+    "**4. Dynamic metric analysis**\n",
     "- Group related metrics (e.g., all LSTM layer gradients)\n",
     "- Create automatically updating charts by [selecting metrics dynamically](https://docs.neptune.ai/chart_widget/#dynamic-metric-selection)\n",
     "- Quickly identify vanishing/exploding gradients\n",
@@ -417,7 +417,7 @@
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
-    "#### 5. Document Training Insights\n",
+    "**5. Document training insights**\n",
     "- Create [custom reports](https://docs.neptune.ai/reports) to track training progress\n",
     "- Document anomalies and successful configurations\n",
     "- Maintain training history\n",
@@ -430,7 +430,7 @@
     "\n",
     "---\n",
     "\n",
-    "**Additional Resources:**\n",
+    "**Additional resources:**\n",
     "- [PyTorch Layer-wise Tracking Package](TODO:Link to integration for tracking layer-wise metrics)\n"
    ]
   }

From 0b8847b407428f45f42c5c1816e742c6e57c0ee6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 16 Apr 2025 14:28:30 +0200
Subject: [PATCH 101/125] style: Update markdown section of explanations for
 better readability and flow

---
 .../debug_training_runs.ipynb                 | 32 ++++++++++++-------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 0509f9f..73f72da 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Debug Model Training with Neptune\n",
+    "# Debug Model Training Runs with Neptune\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
@@ -36,7 +36,9 @@
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
-    "_Note: This is a code recipe that you can adapt for your own model training needs._"
+    "_Note: This is a code recipe that you can adapt for your own model training needs._\n",
+    "\n",
+    "If you are using Google Colab to run this example - please ensure that you update the runtime to use the free available GPU."
    ]
   },
   {
@@ -75,7 +77,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Install dependencies and import libraries"
+    "### Install dependencies and import libraries\n",
+    "\n",
+    "This tutorial uses a datset from Hugging Face which is loaded using the `datasets` package from Hugging Face."
    ]
   },
   {
@@ -105,15 +109,13 @@
    "source": [
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 8,\n",
-    "    \"learning_rate\": 0.01,\n",
+    "    \"batch_size\": 8, # Can be increased to 32 when training via Google Colab\n",
+    "    \"learning_rate\": 0.01, # A smaller lr can be used \n",
     "    \"epochs\": 5,\n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
     "    \"embed_size\": 1000,\n",
     "    \"hidden_size\": 256,  # hidden size for the LSTM\n",
-    "    \"dropout_prob\": 0.3,\n",
-    "    \"num_lstm_layers\": 3,\n",
     "}"
    ]
   },
@@ -260,7 +262,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Debug model training run with Neptune"
+    "## Debug model training run with Neptune\n",
+    "\n",
+    "In this section, we'll walk through 4 key steps to effectively debug your training runs:\n",
+    "\n",
+    "1. **Initialize Neptune** - set up the Neptune environment to track your training metrics\n",
+    "2. **Log configuration parameters** - record your model's hyperparameters and setup\n",
+    "3. **Track layer-wise metrics** - monitor gradient norms across all model layers\n",
+    "4. **Analyze training behavior** - Use Neptune's visualization tools to identify and diagnose training issues"
    ]
   },
   {
@@ -291,7 +300,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 2: _Log configuration parameters and tags_"
+    "### Step 2: _Log configuration parameters_"
    ]
   },
   {
@@ -306,7 +315,6 @@
     "        \"config/optimizer\": params[\"optimizer\"],\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/num_lstm_layers\": params[\"num_lstm_layers\"],\n",
     "        \"data/embed_size\": params[\"embed_size\"],\n",
     "    }\n",
     ")\n",
@@ -320,7 +328,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 3: _Execute model training loop_\n",
+    "### Step 3: _Track gradient norms during training_\n",
     "\n",
     "In this training loop, we:\n",
     "1. Register backward hooks to capture gradient norms from all model layers with `register_full_backward_hook()`\n",
@@ -382,7 +390,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 4: Analyze and debug training\n",
+    "### Step 4: _Analyze training behavior_\n",
     "While your model trains, use Neptune's web interface to monitor and analyze metrics in near real-time:\n",
     "\n",
     "**1. Real-time metric visualization**\n",

From 69bdb16a697663fa37ab55ae0acb1798ad1ab610 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 16 Apr 2025 14:30:49 +0200
Subject: [PATCH 102/125] chore: pre-commit cleanup

---
 .../debug_training_runs.ipynb                 | 21 +++++--------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 73f72da..67874ba 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -109,8 +109,8 @@
    "source": [
     "params = {\n",
     "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 8, # Can be increased to 32 when training via Google Colab\n",
-    "    \"learning_rate\": 0.01, # A smaller lr can be used \n",
+    "    \"batch_size\": 8,  # Can be increased to 32 when training via Google Colab\n",
+    "    \"learning_rate\": 0.01,  # A smaller lr can be used\n",
     "    \"epochs\": 5,\n",
     "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
     "    \"input_features\": 256,\n",
@@ -137,21 +137,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training samples: 81926\n",
-      "Vocabulary size: 128257\n",
-      "Model created: MultilayerModel\n",
-      "Optimizer: Adam\n",
-      "Criterion: CrossEntropyLoss\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -191,6 +179,7 @@
     ")\n",
     "print(f\"Vocabulary size: {params['vocab_size']}\")\n",
     "\n",
+    "\n",
     "# Create model\n",
     "class MultilayerModel(nn.Module):\n",
     "    \"\"\"A larger language model with multiple LSTM and fully connected layers.\"\"\"\n",

From 0fed41210864bd173b0502ec8f7333a382b4d8d5 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 16 Apr 2025 14:49:54 +0200
Subject: [PATCH 103/125] chore: removed unused code for this example

---
 .../pytorch/notebooks/README.md               | 181 -------
 .../pytorch/notebooks/TorchWatcher.py         | 358 -------------
 .../pytorch_text_model_debugging.ipynb        | 487 ------------------
 .../notebooks/torch_watcher_example.py        | 144 ------
 4 files changed, 1170 deletions(-)
 delete mode 100644 integrations-and-supported-tools/pytorch/notebooks/README.md
 delete mode 100644 integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
 delete mode 100644 integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
 delete mode 100644 integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py

diff --git a/integrations-and-supported-tools/pytorch/notebooks/README.md b/integrations-and-supported-tools/pytorch/notebooks/README.md
deleted file mode 100644
index 67e6bd8..0000000
--- a/integrations-and-supported-tools/pytorch/notebooks/README.md
+++ /dev/null
@@ -1,181 +0,0 @@
-# TorchWatcher
-
-A lightweight PyTorch model monitoring tool that automatically tracks layer activations, gradients, and parameters during training. Built for seamless integration with Neptune.ai.
-
-## Features
-
-- **Automatic Layer Tracking**: Monitors activations, gradients, and parameters of specified PyTorch layers
-- **Flexible Layer Selection**: Track all layers or specify which layer types to monitor
-- **Comprehensive Statistics**: Predefined tensor statistics including mean, standard deviation, norm, min, max, variance, and absolute mean
-- **Configurable Tracking**: Enable/disable tracking of activations, gradients, and parameters as needed
-- **Organized Logging**: Structured metric namespacing for better organization in Neptune
-- **Memory Efficient**: Clears stored tensors after each logging step
-- **Error Handling**: Robust error handling with informative warnings
-
-## Installation
-
-```bash
-pip install neptune_scale
-```
-
-## Usage
-
-### Basic Usage
-
-```python
-from neptune_scale import Run
-from TorchWatcher import TorchWatcher
-
-# Initialize Neptune run
-run = Run(experiment_name="my-experiment")
-
-# Create your PyTorch model
-model = YourModel()
-
-# Initialize TorchWatcher
-watcher = TorchWatcher(
-    model,
-    run,
-    track_layers=[nn.Linear, nn.ReLU],  # Specify which layer types to track (default is all layers)
-    tensor_stats=['mean', 'norm'],       # Choose which statistics to compute (default = "mean" only)
-    base_namespace="model_metrics"       # Set base namespace for all metrics (default = "debug")
-)
-
-# Training loop
-for epoch in range(n_epochs):
-    for batch in train_loader:
-        # Forward pass
-        output = model(batch)
-
-        # Backward pass
-        loss.backward()
-
-        # Track metrics with default namespace
-        watcher.watch(step=current_step)
-```
-
-### Controlling Metric Logging
-
-The `watch()` method provides flexible control over what metrics to track and how to organize them:
-
-```python
-# Track all metrics (default behavior)
-watcher.watch(step=current_step)
-
-# Track specific metrics
-watcher.watch(
-    step=current_step,
-    track_activations=True,
-    track_gradients=False,
-    track_parameters=True
-)
-
-# Use a namespace to organize metrics
-watcher.watch(
-    step=current_step,
-    namespace="train"  # Metrics will be under "train/model_metrics/..."
-)
-```
-
-### Namespace Organization
-
-TorchWatcher provides a hierarchical namespace structure for organizing metrics:
-
-1. **Base Namespace**: Set during initialization
-```python
-watcher = TorchWatcher(
-    model,
-    run,
-    base_namespace="model_metrics"  # All metrics will be under "model_metrics/"
-)
-```
-
-2. **Per-Call Namespace**: Prefix for specific tracking calls
-```python
-# During training
-watcher.watch(step=step, namespace="train")  # Metrics under "train/model_metrics/"
-
-# During validation
-watcher.watch(step=step, namespace="validation")  # Metrics under "validation/model_metrics/"
-```
-
-3. **Metric Structure**: Metrics are organized as:
-```
-{namespace}/{base_namespace}/{metric_type}/{layer_name}_{statistic}
-```
-
-Example metric names:
-- `train/model_metrics/activation/fc1_mean`
-- `validation/model_metrics/gradient/fc2_norm`
-- `train/model_metrics/parameters/fc1_weight_mean`
-
-### Example Use Cases
-
-1. **Training with Full Tracking**:
-```python
-# Track everything during initial training
-watcher.watch(step=step, namespace="train")
-```
-
-2. **Validation with Limited Tracking**:
-```python
-# Track only activations during validation
-watcher.watch(
-    step=step,
-    track_activations=True,
-    track_gradients=False,
-    track_parameters=False,
-    namespace="validation"
-)
-```
-
-3. **Efficient Training**:
-```python
-# Track only gradients during later training phases
-watcher.watch(
-    step=step,
-    track_activations=False,
-    track_parameters=False,
-    track_gradients=True,
-    namespace="train"
-)
-```
-
-## Supported Layer Types
-
-TorchWatcher supports tracking of all common PyTorch layer types, including:
-- Linear layers
-- Convolutional layers
-- Recurrent layers
-- Normalization layers
-- Activation layers
-- Pooling layers
-- Dropout layers
-- Embedding layers
-- Transformer layers
-- Attention layers
-- And more...
-
-## Available Statistics
-
-Predefined tensor statistics include:
-- `mean`: Mean value
-- `std`: Standard deviation
-- `norm`: L2 norm
-- `min`: Minimum value
-- `max`: Maximum value
-- `var`: Variance
-- `abs_mean`: Mean of absolute values
-
-## Example
-
-See `torch_watcher_example.py` for a complete example demonstrating:
-- Model definition
-- Data generation
-- Training loop with different tracking configurations
-- Namespace organization
-- Integration with Neptune
-
-## License
-
-MIT License
diff --git a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py b/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
deleted file mode 100644
index 6693859..0000000
--- a/integrations-and-supported-tools/pytorch/notebooks/TorchWatcher.py
+++ /dev/null
@@ -1,358 +0,0 @@
-import warnings
-from typing import Any, Dict, List, Literal, Optional, Type, Union
-
-import torch
-import torch.nn as nn
-
-# Predefined tensor statistics
-TENSOR_STATS = {
-    "mean": lambda x: x.mean().item(),
-    "std": lambda x: x.std().item(),
-    "norm": lambda x: x.norm().item(),
-    "min": lambda x: x.min().item(),
-    "max": lambda x: x.max().item(),
-    "var": lambda x: x.var().item(),
-    "abs_mean": lambda x: x.abs().mean().item(),
-}
-
-# Common PyTorch layer types for validation
-PYTORCH_LAYERS = {
-    # Linear layers
-    nn.Linear,
-    # Convolutional layers
-    nn.Conv1d,
-    nn.Conv2d,
-    nn.Conv3d,
-    nn.ConvTranspose1d,
-    nn.ConvTranspose2d,
-    nn.ConvTranspose3d,
-    # Recurrent layers
-    nn.LSTM,
-    nn.GRU,
-    nn.RNN,
-    # Normalization layers
-    nn.BatchNorm1d,
-    nn.BatchNorm2d,
-    nn.BatchNorm3d,
-    nn.LayerNorm,
-    nn.InstanceNorm1d,
-    nn.InstanceNorm2d,
-    nn.InstanceNorm3d,
-    # Activation layers
-    nn.ReLU,
-    nn.LeakyReLU,
-    nn.ELU,
-    nn.SELU,
-    nn.GELU,
-    # Pooling layers
-    nn.MaxPool1d,
-    nn.MaxPool2d,
-    nn.MaxPool3d,
-    nn.AvgPool1d,
-    nn.AvgPool2d,
-    nn.AvgPool3d,
-    # Dropout layers
-    nn.Dropout,
-    nn.Dropout2d,
-    nn.Dropout3d,
-    # Embedding layers
-    nn.Embedding,
-    nn.EmbeddingBag,
-    # Transformer layers
-    nn.TransformerEncoderLayer,
-    nn.TransformerDecoderLayer,
-    # Attention layers
-    nn.MultiheadAttention,
-    # Flatten layers
-    nn.Flatten,
-    nn.Unflatten,
-    # Other common layers
-    nn.Sequential,
-    nn.ModuleList,
-    nn.ModuleDict,
-}
-
-
-class HookManager:
-    """
-    A robust hook management class for PyTorch models to track activations, gradients, and parameters.
-
-    Improvements:
-    - More comprehensive error handling
-    - Flexible hook registration
-    - Support for more layer types
-    - Configurable tracking
-    """
-
-    def __init__(self, model: nn.Module, track_layers: Optional[List[Type[nn.Module]]] = None):
-        """
-        Initialize HookManager with layer types to track.
-
-        Args:
-            model (nn.Module): The PyTorch model to track
-            track_layers (Optional[List[Type[nn.Module]]]): List of PyTorch layer types to track.
-                                                          If None, tracks all layers in the model.
-                                                          If specified, must contain valid PyTorch layer types.
-
-        Raises:
-            TypeError: If model is not a PyTorch model
-            ValueError: If track_layers contains invalid layer types
-        """
-        if not isinstance(model, nn.Module):
-            raise TypeError("The model must be a PyTorch model")
-
-        # Validate that all specified layers are valid PyTorch layers if track_layers is provided
-        if track_layers is not None:
-            invalid_layers = [layer for layer in track_layers if layer not in PYTORCH_LAYERS]
-            if invalid_layers:
-                raise ValueError(
-                    f"Invalid layer types specified: {invalid_layers}. "
-                    f"Please use valid PyTorch layer types from torch.nn."
-                )
-
-        self.model = model
-        self.hooks: List[torch.utils.hooks.RemovableHandle] = []
-        self.activations: Dict[str, torch.Tensor] = {}
-        self.gradients: Dict[str, torch.Tensor] = {}
-        self.track_layers = track_layers
-
-    def save_activation(self, name: str):
-        """Create a forward hook to save layer activations."""
-
-        def hook(module, input, output):
-            try:
-                # Handle different output types (tensor or tuple)
-                activation = output[0] if isinstance(output, tuple) else output
-                self.activations[name] = activation.detach()
-            except Exception as e:
-                warnings.warn(f"Could not save activation for {name}: {e}")
-
-        return hook
-
-    def save_gradient(self, name: str):
-        """Create a backward hook to save layer gradients."""
-
-        def hook(module, grad_input, grad_output):
-            try:
-                # Save the first gradient output
-                self.gradients[name] = grad_output[0].detach()
-            except Exception as e:
-                warnings.warn(f"Could not save gradient for {name}: {e}")
-
-        return hook
-
-    def register_hooks(self, track_activations: bool = True, track_gradients: bool = True):
-        """
-        Register hooks for the model with configurable tracking.
-
-        Args:
-            track_activations (bool): Whether to track layer activations
-            track_gradients (bool): Whether to track layer gradients
-        """
-        # Clear existing hooks
-        self.remove_hooks()
-
-        # Register forward hooks for activations
-        if track_activations:
-            for name, module in self.model.named_modules():
-                # Skip the model itself
-                if name == "":
-                    continue
-                # Track all layers if track_layers is None, otherwise only specified types
-                if self.track_layers is None or any(
-                    isinstance(module, layer_type) for layer_type in self.track_layers
-                ):
-                    hook = module.register_forward_hook(self.save_activation(name))
-                    self.hooks.append(hook)
-
-        # Register backward hooks for gradients
-        if track_gradients:
-            for name, module in self.model.named_modules():
-                # Skip the model itself
-                if name == "":
-                    continue
-                # Track all layers if track_layers is None, otherwise only specified types
-                if self.track_layers is None or any(
-                    isinstance(module, layer_type) for layer_type in self.track_layers
-                ):
-                    hook = module.register_full_backward_hook(self.save_gradient(name))
-                    self.hooks.append(hook)
-
-    def remove_hooks(self):
-        """Remove all registered hooks."""
-        for hook in self.hooks:
-            hook.remove()
-        self.hooks.clear()
-
-    def clear(self):
-        """Clear stored activations and gradients."""
-        self.activations.clear()
-        self.gradients.clear()
-
-    def get_activations(self) -> Dict[str, torch.Tensor]:
-        """Get stored activations."""
-        return self.activations
-
-    def get_gradients(self) -> Dict[str, torch.Tensor]:
-        """Get stored gradients."""
-        return self.gradients
-
-    def __del__(self):
-        """Ensure hooks are removed when the object is deleted."""
-        self.remove_hooks()
-
-
-class TorchWatcher:
-    """
-    A comprehensive tracking mechanism for PyTorch models with enhanced logging.
-    """
-
-    def __init__(
-        self,
-        model: nn.Module,
-        run: Any,  # Made more flexible to support different logging mechanisms
-        track_layers: Optional[List[Type[nn.Module]]] = None,
-        tensor_stats: Optional[List[str]] = None,
-        base_namespace: str = "debug",  # Default namespace for all metrics
-    ) -> None:
-        """
-        Initialize TorchWatcher with configuration options.
-
-        Args:
-            model (nn.Module): The PyTorch model to watch
-            run: Logging mechanism from Neptune
-            track_layers (Optional[List[Type[nn.Module]]]): List of PyTorch layer types to track.
-                                                          If None, tracks all layers in the model.
-                                                          If specified, must contain valid PyTorch layer types.
-            tensor_stats (Optional[List[str]]): List of statistics to compute.
-                                              Available options: mean, std, norm, min, max, var, abs_mean.
-                                              Defaults to ['mean'] if not specified.
-            base_namespace (str): Base namespace for all logged metrics. Defaults to "debug".
-
-        Raises:
-            TypeError: If model is not a PyTorch model
-            ValueError: If track_layers contains invalid layer types
-        """
-        if not isinstance(model, nn.Module):
-            raise TypeError("The model must be a PyTorch model")
-
-        self.model = model
-        self.run = run
-        self.hm = HookManager(model, track_layers)
-        self.debug_metrics: Dict[str, float] = {}
-        self.base_namespace = base_namespace
-
-        # Validate and set tensor statistics
-        if tensor_stats is None:
-            tensor_stats = ["mean"]
-
-        # Validate that all requested statistics exist
-        # TODO: Add ability to set custom statistics
-        invalid_stats = [stat for stat in tensor_stats if stat not in TENSOR_STATS]
-        if invalid_stats:
-            raise ValueError(
-                f"Invalid statistics requested: {invalid_stats}. "
-                f"Available statistics are: {list(TENSOR_STATS.keys())}"
-            )
-
-        self.tensor_stats = {stat: TENSOR_STATS[stat] for stat in tensor_stats}
-
-        # Default hook registration
-        self.hm.register_hooks()
-
-    def _safe_tensor_stats(self, tensor: torch.Tensor) -> Dict[str, float]:
-        """
-        Safely compute tensor statistics with error handling.
-
-        Args:
-            tensor (torch.Tensor): Input tensor
-
-        Returns:
-            Dict of statistical metrics
-        """
-        stats = {}
-        for stat_name, stat_func in self.tensor_stats.items():
-            try:
-                stats[stat_name] = stat_func(tensor)
-            except Exception as e:
-                warnings.warn(f"Could not compute {stat_name} statistic: {e}")
-        return stats
-
-    def _track_metric(
-        self, metric_type: str, data: Dict[str, torch.Tensor], namespace: Optional[str] = None
-    ):
-        """Track metrics with enhanced statistics for a given metric type.
-
-        Args:
-            metric_type (str): Type of metric being tracked (activation/gradient/parameters)
-            data (Dict[str, torch.Tensor]): Dictionary mapping layer names to tensors
-            namespace (Optional[str]): Optional namespace to prefix the base namespace
-        """
-        # Construct the full namespace
-        full_namespace = f"{namespace}/{self.base_namespace}" if namespace else self.base_namespace
-
-        for layer, tensor in data.items():
-            if tensor is not None:
-                stats = self._safe_tensor_stats(tensor)
-                for stat_name, stat_value in stats.items():
-                    self.debug_metrics[f"{full_namespace}/{metric_type}/{layer}_{stat_name}"] = (
-                        stat_value
-                    )
-
-    def track_activations(self, namespace: Optional[str] = None):
-        """Track layer activations with enhanced statistics."""
-        activations = self.hm.get_activations()
-        self._track_metric("activation", activations, namespace)
-
-    def track_gradients(self, namespace: Optional[str] = None):
-        """Track layer gradients with enhanced statistics."""
-        gradients = self.hm.get_gradients()
-        self._track_metric("gradient", gradients, namespace)
-
-    def track_parameters(self, namespace: Optional[str] = None):
-        """Track model parameters with enhanced statistics."""
-        parameters = {
-            name: param.grad
-            for name, param in self.model.named_parameters()
-            if param is not None and param.grad is not None
-        }
-        self._track_metric("parameters", parameters, namespace)
-
-    def watch(
-        self,
-        step: Union[int, float],
-        track_gradients: bool = True,
-        track_parameters: bool = True,
-        track_activations: bool = True,
-        namespace: Optional[str] = None,
-    ):
-        """
-        Log debug metrics with flexible configuration.
-
-        Args:
-            step (int|float): Logging step
-            track_gradients (bool): Whether to track gradients. Defaults to True.
-            track_parameters (bool): Whether to track parameters. Defaults to True.
-            track_activations (bool): Whether to track activations. Defaults to True.
-            namespace (Optional[str]): Optional namespace to prefix the base namespace.
-                                     If provided, metrics will be logged under {namespace}/{base_namespace}/...
-        """
-        # Reset metrics
-        self.debug_metrics.clear()
-
-        # Track metrics based on boolean flags
-        if track_gradients:
-            self.track_gradients(namespace)
-        if track_parameters:
-            self.track_parameters(namespace)
-        if track_activations:
-            self.track_activations(namespace)
-
-        # Log metrics
-        try:
-            self.run.log_metrics(data=self.debug_metrics, step=step)
-        except Exception as e:
-            warnings.warn(f"Logging failed: {e}")
-
-        # Clear hooks
-        self.hm.clear()
diff --git a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb b/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
deleted file mode 100644
index 1b1b03c..0000000
--- a/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Neptune + PyTorch\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lr%2Fpytorch_example/integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb\"> \n",
-    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
-    "</a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction\n",
-    "Global metrics such as loss or accuracy provide a high-level performance snapshot and ensure training is on course.\n",
-    "\n",
-    "However, for large or foundation models, monitoring layer-wise metrics—such as gradients and activations—delivers critical insights into how each layer learns. This level of detail helps identify issues and fine-tune individual layers for better overall performance.\n",
-    "The main challenge is the volume of data generated by layer-wise logging.\n",
-    "\n",
-    "Fortunately, Neptune is built for hyperscale tracking. It enables you to capture, organize, and analyze metrics from every layer without disrupting the training process. No matter how large is your model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This guide will show you how to:\n",
-    "- Initialize the **Neptune Run** object and log configuration parameters\n",
-    "- Create a **reusable class** to hook and log layer-wise metrics (`TorchWatcher`)\n",
-    "- Log **aggregated metrics** such as loss and accuracy\n",
-    "- Log **layer-wise metrics** to debug model training such as:\n",
-    "\n",
-    "| **Metric**                        | **Demonstrated** | **What it shows**                                                                                             | **How to capture**                                             |\n",
-    "|-----------------------------------|--------------------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|\n",
-    "| **Activations**                   | Yes                                  | Dead or exploding activations can indicate issues with training stability. | `TorchWatcher`            |\n",
-    "| **Gradients**                     | Yes                                  | Essential for diagnosing vanishing or exploding gradients. Small gradients may indicate vanishing gradients, while large ones can signal instability. | `TorchWatcher`        |\n",
-    "| **Parameters**            | Yes                                  | Tracks how the model’s parameters evolve during training. Large or small weights may indicate the need for better regularization or adjustments in learning rate. | Extract directly from the model’s parameters.                  |\n",
-    "| **Loss**               | No                                   | Identifies which parts of the network contribute more to the overall loss, aiding debugging and optimization. | Monitor outputs from each layer and compare with the target.   |\n",
-    "| **Learning rate**       | No                                   | Helpful if using techniques like Layer-wise Learning Rate Decay (L2LRD). Tracking this can provide insight into the layer-specific learning rate. | Manually track based on optimizer settings.                    |\n",
-    "| **Output norms**            | No                                   | The L2-norm of layer outputs can highlight issues like gradient explosion or vanishing gradients. | Compute the L2-norm for each layer’s output.                   |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use this notebook as a code recipe. Add your own code and adapt the sections to your own model training needs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Before you start\n",
-    "\n",
-    "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
-    "  2. Create a Neptune project for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
-    "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set environment variables\n",
-    "By setting your project name and API token as environment variables, you can use them throughout this notebook.\n",
-    "\n",
-    "Uncomment the code block below and replace placeholder values with your own credentials:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set Neptune credentials as environment variables\n",
-    "# %env NEPTUNE_API_TOKEN = \"your_api_token\"\n",
-    "# %env NEPTUNE_PROJECT = \"your_workspace_name_here/your_project_name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Install dependencies and import libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install dependencies\n",
-    "! pip install -qU neptune_scale torch datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import libraries\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "import numpy as np\n",
-    "from collections import Counter\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "from neptune_scale import Run\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Initialize parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize model, loss function, and optimizer\n",
-    "\n",
-    "params = {\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 8,\n",
-    "    \"learning_rate\": 0.01,\n",
-    "    \"epochs\": 5,\n",
-    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
-    "    \"input_features\": 256,\n",
-    "    \"embed_size\": 1000,\n",
-    "    \"hidden_size\": 256,  # hidden size for the LSTM\n",
-    "    \"dropout_prob\": 0.3,\n",
-    "    \"num_lstm_layers\": 3,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download or use next token prediction dataset\n",
-    "This example uses the dataset from [HuggingFace](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset) (HF).\n",
-    "\n",
-    "You can increase the size of the dataset to test the logging capabilities of Neptune. Note that increasing the size will increase the time needed for the dataset to download. The current setup only downloads the first parquet file from the Hugging Face public dataset.\n",
-    "\n",
-    "The validation dataset is also reduced to decrease the training loop execution time. To increase the validation size, change the `test_size` key-value pair in the `train_test_split()` method from HuggingFace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# For the example, download a random subset of 10% of the original dataset\n",
-    "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
-    "data_files = {\n",
-    "    \"train\": base_url\n",
-    "    + \"train-00001-of-00067.parquet\",  # download only the first 10 files from the HF dataset\n",
-    "    \"validation\": base_url + \"validation-00000-of-00001.parquet\",\n",
-    "}  # download the complete validation dataset\n",
-    "\n",
-    "data_subset = load_dataset(\"parquet\", data_files=data_files, num_proc=4)\n",
-    "# validation_subset = load_dataset(\"parquet\", data_files = {\"validation\": base_url + \"validation-00000-of-00001.parquet\"}, num_proc=4, split=[\"validation[:5%]\"])\n",
-    "validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
-    "print(\n",
-    "    f\"Training samples: {data_subset['train'].num_rows} \\nValidation samples: {validation_subset['test'].num_rows}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create `DataLoader` objects\n",
-    "To execute the models with PyTorch, convert the training and validation datasets to tensors. Then, set up `DataLoader` for easier batching in the training loop.\n",
-    "\n",
-    "The model architecture requires the vocabulary size as an input and this is why we calculate the max token from the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_subset = data_subset[\"train\"].with_format(\n",
-    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
-    ")  # HF provides methods to convert data types to tensors\n",
-    "validation_subset = validation_subset[\"test\"].with_format(\n",
-    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
-    ")  # HF provides methods to convert data types to tensors\n",
-    "\n",
-    "train_dataloader = DataLoader(train_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
-    "val_dataloader = DataLoader(validation_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
-    "\n",
-    "# Determine the vocab size of the dataset\n",
-    "# Flatten the list of tokenized sentences into one long list of token IDs\n",
-    "vocab_size = (\n",
-    "    max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
-    ")\n",
-    "params[\"vocab_size\"] = vocab_size\n",
-    "print(f\"Vocabulary size: {vocab_size}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Define PyTorch model architecture and helpers\n",
-    "Define a simple LLM model architecture using PyTorch. Since this is a text-based example, we use an embedding layer, a LSTM layer, and a fully connected layer.\n",
-    "\n",
-    "You can adjust this architecture to your needs and increase its size when testing the workflow:\n",
-    "- To increase the size of the LSTM layers, change the `num_layers` parameter in the parameters dictionary.\n",
-    "- To increase the number of fully connected layers, update the mode architecture itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the simple LLM model with LSTM\n",
-    "class SimpleLLM(nn.Module):\n",
-    "    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):\n",
-    "        super(SimpleLLM, self).__init__()\n",
-    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers=num_layers, batch_first=True)\n",
-    "        self.fc1 = nn.Linear(hidden_size, vocab_size)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.embedding(x)\n",
-    "        lstm_out, _ = self.lstm(x)  # LSTM returns output and hidden/cell state tuple\n",
-    "        out = self.fc1(lstm_out)  # Use the last output from the LSTM\n",
-    "        return out\n",
-    "\n",
-    "\n",
-    "# Function to evaluate the model after each epoch/step\n",
-    "def evaluate(model, val_dataloader, criterion, device, vocab_size):\n",
-    "    model.eval()  # Set the model to evaluation mode\n",
-    "    total_loss = 0\n",
-    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
-    "        for batch in val_dataloader:\n",
-    "            input_ids = batch[\"input_ids\"].to(device)\n",
-    "            labels = batch[\"labels\"].to(device)\n",
-    "\n",
-    "            # Forward pass for validation\n",
-    "            logits = model(input_ids)  # Shape: (batch_size, seq_len, vocab_size)\n",
-    "\n",
-    "            # Calculate the loss\n",
-    "            loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "            total_loss += loss.item()\n",
-    "\n",
-    "    avg_val_loss = total_loss / len(val_dataloader)\n",
-    "    return avg_val_loss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup tracking class\n",
-    "\n",
-    "This section intializes the `TorchWatcher` class:\n",
-    "- It accepts a PyTorch model object as an input. \n",
-    "- It allows you to capture the **parameters**, **activations** and **gradients** from each layer.\n",
-    "- Specify which tensor statistics to capture.\n",
-    "\n",
-    "See a pseudo implementation:\n",
-    "\n",
-    "```python\n",
-    "from TorchWatcher import TorchWatcher\n",
-    "\n",
-    "model = YourModel()\n",
-    "watcher = TorchWatcher(model, run)  # Uses default mean() statistic\n",
-    "\n",
-    "# Training loop\n",
-    "for epoch in range(3):\n",
-    "    # Forward pass\n",
-    "    output = model(x_batch)\n",
-    "    loss = criterion(output, y_batch)\n",
-    "\n",
-    "    # Backward pass\n",
-    "    optimizer.zero_grad()\n",
-    "    loss.backward()\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    # Track metrics after the forward and backward passes\n",
-    "    watcher.watch(step=epoch)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from TorchWatcher import TorchWatcher"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up model training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Initialize Neptune run object and log hyperparameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from neptune_scale import Run\n",
-    "from uuid import uuid4\n",
-    "\n",
-    "custom_run_id = f\"pytorch-text-{uuid4()}\"  # Create your own custom run_id\n",
-    "experiment_name = \"pytorch-text\"  # Create a run that is the head of an experiment. This will also be used for forking.\n",
-    "\n",
-    "run = Run(\n",
-    "    run_id=custom_run_id,\n",
-    "    experiment_name=experiment_name,\n",
-    ")\n",
-    "\n",
-    "run.log_configs(\n",
-    "    {\n",
-    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
-    "        \"config/optimizer\": params[\"optimizer\"],\n",
-    "        \"config/batch_size\": params[\"batch_size\"],\n",
-    "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/num_lstm_layers\": params[\"num_lstm_layers\"],\n",
-    "        \"data/vocab_size\": params[\"vocab_size\"],\n",
-    "        \"data/embed_size\": params[\"embed_size\"],\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "run.add_tags(tags=[params[\"optimizer\"]], group_tags=True)\n",
-    "run.add_tags(tags=[\"text\", \"LLM\", \"Simple\"])\n",
-    "\n",
-    "print(run.get_experiment_url())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Execute model training loop\n",
-    "\n",
-    "In the training loop we call the `watch()` method from the `TorchWatcher` package after the backward and forward passes to track our parameters, gradients and activations with a combination of tensor statistics. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize model and optimizer\n",
-    "model = SimpleLLM(\n",
-    "    params[\"vocab_size\"], params[\"embed_size\"], params[\"hidden_size\"], params[\"num_lstm_layers\"]\n",
-    ")\n",
-    "optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
-    "criterion = nn.CrossEntropyLoss(\n",
-    "    ignore_index=-100\n",
-    ")  # Ignore the buffering index of -100 in the dataset\n",
-    "\n",
-    "# Define watcher class\n",
-    "watcher = TorchWatcher(\n",
-    "    model=model, run=run, tensor_stats=[\"mean\", \"norm\"], base_namespace=\"debug_metrics\"\n",
-    ")\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model.to(device)\n",
-    "step_counter = 0\n",
-    "\n",
-    "# Training loop\n",
-    "for epoch in range(params[\"epochs\"]):\n",
-    "    total_loss = 0\n",
-    "    for batch in train_dataloader:\n",
-    "        model.train()\n",
-    "        step_counter += 1\n",
-    "\n",
-    "        input_ids = batch[\"input_ids\"].to(device)\n",
-    "        labels = batch[\"labels\"].to(device)\n",
-    "\n",
-    "        optimizer.zero_grad()\n",
-    "\n",
-    "        # Forward pass\n",
-    "        logits = model(input_ids)\n",
-    "\n",
-    "        # Compute the loss (ignore padding tokens by masking labels)\n",
-    "        loss = criterion(logits.view(-1, vocab_size), labels.view(-1))\n",
-    "\n",
-    "        # Backward pass and optimization\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        total_loss += loss.item()\n",
-    "        print(f\"Step {step_counter} / {len(train_dataloader)}, Loss: {loss.item()}\")\n",
-    "\n",
-    "        # Call watch() method in loop determing which layer-wise metrics to watch\n",
-    "        watcher.watch(step=step_counter, track_activations=False, track_parameters=False)\n",
-    "\n",
-    "        if step_counter % 50 == 0:  # Log validation loss at every 50 steps\n",
-    "            val_loss = evaluate(model, val_dataloader, criterion, device, vocab_size)\n",
-    "\n",
-    "            run.log_metrics(\n",
-    "                data={\n",
-    "                    \"metrics/train/loss\": loss.item(),\n",
-    "                    \"metrics/validation/loss\": val_loss,\n",
-    "                    \"epoch/value\": epoch,\n",
-    "                },\n",
-    "                step=step_counter,\n",
-    "            )\n",
-    "        else:  # Log training loss and debugging metrics for each step\n",
-    "            run.log_metrics(\n",
-    "                data={\"metrics/train/loss\": loss.item(), \"epoch/value\": epoch},\n",
-    "                step=step_counter,\n",
-    "            )\n",
-    "\n",
-    "    print(f\"Epoch {epoch + 1}, Loss: {total_loss / len(train_dataloader)}\")\n",
-    "\n",
-    "# Close run to ensure all operations are processed\n",
-    "run.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What's next?\n",
-    "While the model is training, you can start using the Neptune web app to browse your metrics and create custom analyses and visualizations:\n",
-    "1. To visualize the large number of metrics being logged in near real time, navigate to the **Charts** tab of the active run.\n",
-    "2. Filter the metrics using the [advanced regex searching capabilities](https://docs-beta.neptune.ai/charts#filtering-charts). For example, enter `.*gradient+.*fc\\d` in the search bar. This query filters all metrics for the gradients of the fully connected layers. The more FC layers, the more charts will appear.\n",
-    "3. Export the filter to a [dashboard](https://docs-beta.neptune.ai/custom_dashboard). The saved dashboard will now only display these metrics during training.\n",
-    "4. Use the [dynamic metric selection](https://docs-beta.neptune.ai/chart_widget#dynamic-metric-selection) and update the chart widget to display all fully connected layers gradients in one chart. Again, use the `.*gradient+.*fc\\d` query.\n",
-    "5. Create a [custom report](https://docs-beta.neptune.ai/reports) to outline the model training, global metrics, debugging metrics, and more.\n",
-    "\n",
-    "See also [a generic example of the training result](https://scale.neptune.ai/o/examples/org/LLM-Pretraining/reports/9e6a2cad-77e7-42df-9d64-28f07d37e908).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "neptune_scale_py_312_base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py b/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
deleted file mode 100644
index 1addce3..0000000
--- a/integrations-and-supported-tools/pytorch/notebooks/torch_watcher_example.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from neptune_scale import Run
-from TorchWatcher import TorchWatcher
-
-
-# Define a simple neural network
-class SimpleNet(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 1)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-
-def generate_data(n_samples=1000):
-    """Generate synthetic data for a regression task."""
-    # Generate random input features
-    X = torch.randn(n_samples, 10)
-
-    # Create target values with some non-linear relationship
-    y = (X[:, 0] ** 2 + 0.5 * X[:, 1] + 0.1 * torch.sum(X[:, 2:], dim=1)).unsqueeze(1)
-
-    # Add some noise
-    y += 0.1 * torch.randn_like(y)
-
-    return X, y
-
-
-def train_model(model, X_train, y_train, X_val, y_val, watcher, n_epochs=50, batch_size=32):
-    """Training function demonstrating different ways to use TorchWatcher."""
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
-    criterion = nn.MSELoss()
-    n_batches = len(X_train) // batch_size
-
-    for epoch in range(n_epochs):
-        model.train()
-        train_loss = 0.0
-
-        # Training batches
-        for i in range(n_batches):
-            start_idx = i * batch_size
-            end_idx = start_idx + batch_size
-
-            x_batch = X_train[start_idx:end_idx]
-            y_batch = y_train[start_idx:end_idx]
-
-            # Forward pass
-            output = model(x_batch)
-            loss = criterion(output, y_batch)
-
-            # Backward pass
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            # Track metrics during training
-            if epoch < 5:  # First 5 epochs: track everything
-                watcher.watch(step=epoch * n_batches + i, namespace="train")
-            else:  # Later epochs: track only gradients for efficiency
-                watcher.watch(
-                    step=epoch * n_batches + i,
-                    track_activations=False,
-                    track_parameters=False,
-                    track_gradients=True,
-                    namespace="train",
-                )
-
-            train_loss += loss.item()
-
-        # Average training loss
-        train_loss /= n_batches
-
-        # Validation
-        model.eval()
-        with torch.no_grad():
-            val_output = model(X_val)
-            val_loss = criterion(val_output, y_val)
-
-            # Track metrics during validation
-            watcher.watch(
-                step=epoch,
-                track_activations=True,
-                track_gradients=False,
-                track_parameters=False,
-                namespace="validation",
-            )
-
-        # Log metrics
-        watcher.run.log_metrics(
-            data={"train/loss": train_loss, "val/loss": val_loss.item()}, step=epoch
-        )
-
-        if (epoch + 1) % 10 == 0:
-            print(
-                f"Epoch [{epoch+1}/{n_epochs}], "
-                f"Train Loss: {train_loss:.4f}, "
-                f"Val Loss: {val_loss.item():.4f}"
-            )
-
-
-def main():
-    # Initialize Neptune run
-    run = Run(
-        experiment_name="torch-watcher-example",
-    )
-
-    # Generate data
-    X_train, y_train = generate_data(n_samples=1000)
-    X_val, y_val = generate_data(n_samples=200)
-
-    # Create model and watcher
-    model = SimpleNet()
-
-    # Initialize watcher with specific layer types to track
-    watcher = TorchWatcher(
-        model,
-        run,
-        track_layers=[nn.Linear, nn.ReLU],  # Track only Linear and ReLU layers
-        tensor_stats=["mean", "norm"],  # Track mean and norm statistics
-        base_namespace="model_metrics",  # Default namespace for all metrics
-    )
-
-    # Train the model
-    print("\nTraining with TorchWatcher:")
-    print("- Tracking Linear and ReLU layers")
-    print("- Computing mean and norm statistics")
-    print("- Using 'model_metrics' as base namespace")
-    print("- Full tracking during first 5 epochs with 'train/model_metrics' namespace")
-    print("- Gradient-only tracking during later epochs with 'train/model_metrics' namespace")
-    print("- Activation-only tracking during validation with 'validation/model_metrics' namespace")
-
-    train_model(model, X_train, y_train, X_val, y_val, watcher)
-    watcher.run.close()
-
-
-if __name__ == "__main__":
-    main()

From ba14f8f4d1aba47ffc51ae1509d40f908a1f2e1e Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Wed, 16 Apr 2025 14:51:49 +0200
Subject: [PATCH 104/125] chore: update test yaml

---
 .github/workflows/test-notebooks.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test-notebooks.yml b/.github/workflows/test-notebooks.yml
index 06d5be7..ebf686b 100644
--- a/.github/workflows/test-notebooks.yml
+++ b/.github/workflows/test-notebooks.yml
@@ -21,7 +21,7 @@ jobs:
         python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
         notebooks: # Add in alphabetical order
           - how-to-guides/hpo/notebooks/Neptune_HPO.ipynb
-          - integrations-and-supported-tools/pytorch/notebooks/pytorch_text_model_debugging.ipynb
+          - how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
         os: ["${{ inputs.os }}"]
     steps:
       - uses: actions/checkout@main

From 1ab2ed0eeb5c72f464c468a3440e42e25dc53532 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 2 May 2025 11:35:47 +0200
Subject: [PATCH 105/125] refactor: use synthetic simulation data in example to
 illustrate the point of analyzing debugging metrics

---
 .../debug_training_runs.ipynb                 | 301 +++++++-----------
 1 file changed, 111 insertions(+), 190 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 67874ba..79b5857 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -32,13 +32,11 @@
     "3. Analyze the metrics in Neptune's UI to **debug training issues**\n",
     "\n",
     "Step through a pre-configured report:\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9ed051fc-9b16-4c31-a2da-925230c9c360\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
-    "_Note: This is a code recipe that you can adapt for your own model training needs._\n",
-    "\n",
-    "If you are using Google Colab to run this example - please ensure that you update the runtime to use the free available GPU."
+    "_Note: This is a code recipe that you can adapt for your own model training needs._"
    ]
   },
   {
@@ -89,50 +87,27 @@
    "outputs": [],
    "source": [
     "# Install dependencies\n",
-    "! pip install -qU neptune_scale torch datasets\n",
-    "\n",
-    "import torch"
+    "! pip install -qU neptune_scale"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialize parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "params = {\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"batch_size\": 8,  # Can be increased to 32 when training via Google Colab\n",
-    "    \"learning_rate\": 0.01,  # A smaller lr can be used\n",
-    "    \"epochs\": 5,\n",
-    "    \"device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
-    "    \"input_features\": 256,\n",
-    "    \"embed_size\": 1000,\n",
-    "    \"hidden_size\": 256,  # hidden size for the LSTM\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup data, model and other dependencies\n",
+    "### Setup training simulation\n",
+    "\n",
+    "This tutorial uses a simulated training scenario to demonstrate Neptune's debugging capabilities. The simulation generates synthetic training metrics that mimic real model training, including:\n",
+    "- Gradually decreasing loss\n",
+    "- Increasing accuracy\n",
+    "- Random noise and occasional spikes to mimic gradient norm explosions\n",
+    "- Per-layer gradient norms that fluctuate naturally and spike during loss anomalies\n",
     "\n",
     "In the next cell, we'll:\n",
-    "1. Import required PyTorch and HuggingFace libraries\n",
-    "2. Download and load a [next token prediction dataset](https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset) from HuggingFace\n",
-    "3. Create PyTorch DataLoaders for training\n",
-    "4. Calculate vocabulary size for the model\n",
-    "5. Define a multilayer model for training\n",
+    "1. Import required libraries (numpy, json, etc.) \n",
+    "2. Create a `TrainingMetrics` class that simulates training progression\n",
+    "3. Track gradient norms for 20 layers to help identify optimization issues\n",
     "\n",
-    "_You can modify this setup to use your own data and model architecture._"
+    "_You can replace this simulation with your actual model training code._"
    ]
   },
   {
@@ -141,110 +116,84 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from datasets import load_dataset\n",
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "# Download dataset\n",
-    "base_url = \"https://huggingface.co/datasets/Na0s/Next_Token_Prediction_dataset/resolve/main/data/\"\n",
-    "data_files = {\n",
-    "    \"train\": base_url + \"train-00001-of-00067.parquet\",\n",
-    "    \"validation\": base_url + \"validation-00000-of-00001.parquet\",\n",
+    "import numpy as np\n",
+    "import json\n",
+    "from typing import Dict, Any\n",
+    "import math\n",
+    "\n",
+    "\n",
+    "class TrainingMetrics:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        initial_loss: float = 1.0,\n",
+    "        initial_accuracy: float = 0.0,\n",
+    "        noise_scale: float = 0.1,\n",
+    "        loss_trend: float = -0.1,\n",
+    "        accuracy_trend: float = 0.1,\n",
+    "    ):\n",
+    "        self.previous_loss = initial_loss\n",
+    "        self.previous_accuracy = initial_accuracy\n",
+    "        self.noise_scale = noise_scale\n",
+    "        self.loss_trend = loss_trend\n",
+    "        self.accuracy_trend = accuracy_trend\n",
+    "        self.step_count = 1\n",
+    "\n",
+    "        # Random convergence points\n",
+    "        self.target_loss = np.random.uniform(0.0, 1.0)\n",
+    "        self.target_accuracy = np.random.uniform(0.0, 1.0)\n",
+    "\n",
+    "    def update_metrics(self, spikes=True) -> tuple[float, float, dict]:\n",
+    "        \"\"\"Update loss and accuracy using a random walk process with logarithmic trends\"\"\"\n",
+    "        self.step_count += 1\n",
+    "\n",
+    "        # Base loss update with normal progression\n",
+    "        decay_factor = math.log(1 + abs(self.previous_loss - self.target_loss))\n",
+    "        loss_step = self.noise_scale * np.random.randn() + self.loss_trend * decay_factor\n",
+    "        loss_step *= 1 + 0.1 * math.log(self.step_count)\n",
+    "\n",
+    "        # Initialize gradient norms for 20 layers with small random values\n",
+    "        gradient_norms = {\n",
+    "            f\"debug/grad_norm/layer_{i}\": np.random.uniform(0.01, 0.1) for i in range(20)\n",
+    "        }\n",
+    "\n",
+    "        # Check for spike/anomaly (0.01% chance) after step 10k\n",
+    "        if spikes and np.random.random() < 0.0001:  # and self.step_count > 10000:\n",
+    "            # Generate a sudden spike, independent of current loss\n",
+    "            spike_magnitude = np.random.uniform(1, 10)  # Random spike between 1x and 5x\n",
+    "            current_loss = spike_magnitude\n",
+    "\n",
+    "            # When there's a loss spike, create a corresponding spike in a random layer\n",
+    "            spike_layer = np.random.randint(0, 20)\n",
+    "            gradient_norms[f\"debug/grad_norm/layer_{spike_layer}\"] = (\n",
+    "                spike_magnitude * np.random.uniform(0.8, 1.2)\n",
+    "            )\n",
+    "        else:\n",
+    "            # Normal progression\n",
+    "            current_loss = max(0.0, self.previous_loss + loss_step)\n",
+    "            self.previous_loss = current_loss\n",
+    "\n",
+    "        current_accuracy = 1 - current_loss\n",
+    "\n",
+    "        return current_loss, current_accuracy, gradient_norms\n",
+    "\n",
+    "\n",
+    "# Training parameters\n",
+    "params = {\n",
+    "    \"batch_size\": 32,\n",
+    "    \"epochs\": 100,\n",
+    "    \"noise_scale\": np.random.uniform(0.0005, 0.002),\n",
+    "    \"loss_trend\": -np.random.uniform(0, 0.0002),\n",
+    "    \"accuracy_trend\": np.random.uniform(0, 0.0002),\n",
     "}\n",
     "\n",
-    "# Load dataset\n",
-    "data_subset = load_dataset(\"parquet\", data_files=data_files, num_proc=4)\n",
-    "# validation_subset = data_subset.get(\"validation\").train_test_split(test_size=0.1)\n",
-    "\n",
-    "print(f\"Training samples: {data_subset['train'].num_rows}\")\n",
-    "# print(f\"Validation samples: {validation_subset['test'].num_rows}\")\n",
-    "\n",
-    "# Convert to PyTorch format\n",
-    "train_subset = data_subset[\"train\"].with_format(\n",
-    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
-    ")\n",
-    "\"\"\"validation_subset = validation_subset[\"test\"].with_format(\n",
-    "    type=\"torch\", columns=[\"text\", \"input_ids\", \"labels\"]\n",
-    ")\"\"\"\n",
-    "\n",
-    "# Create dataloaders\n",
-    "train_dataloader = DataLoader(train_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
-    "# val_dataloader = DataLoader(validation_subset, batch_size=params[\"batch_size\"], shuffle=True)\n",
-    "\n",
-    "# Calculate vocabulary size\n",
-    "params[\"vocab_size\"] = (\n",
-    "    max([token for sentence in data_subset[\"train\"][\"input_ids\"] for token in sentence]) + 1\n",
-    ")\n",
-    "print(f\"Vocabulary size: {params['vocab_size']}\")\n",
-    "\n",
-    "\n",
-    "# Create model\n",
-    "class MultilayerModel(nn.Module):\n",
-    "    \"\"\"A larger language model with multiple LSTM and fully connected layers.\"\"\"\n",
-    "\n",
-    "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
-    "        super(MultilayerModel, self).__init__()\n",
-    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
-    "\n",
-    "        # Create multiple LSTM layers\n",
-    "        self.lstm_layers = nn.ModuleList(\n",
-    "            [\n",
-    "                nn.LSTM(\n",
-    "                    hidden_size if i > 0 else embed_size,\n",
-    "                    hidden_size,\n",
-    "                    num_layers=1,\n",
-    "                    batch_first=True,\n",
-    "                )\n",
-    "                for i in range(10)  # 10 LSTM layers\n",
-    "            ]\n",
-    "        )\n",
-    "\n",
-    "        # Create multiple fully connected layers\n",
-    "        self.fc_layers = nn.ModuleList(\n",
-    "            [nn.Linear(hidden_size, hidden_size) for _ in range(9)]  # 9 FC layers\n",
-    "        )\n",
-    "\n",
-    "        # Final layer to project back to vocab size\n",
-    "        self.final_layer = nn.Linear(hidden_size, vocab_size)\n",
-    "\n",
-    "        # Add dropout for regularization\n",
-    "        self.dropout = nn.Dropout(0.1)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        # Embedding layer\n",
-    "        x = self.embedding(x)\n",
-    "\n",
-    "        # Process through LSTM layers\n",
-    "        for lstm in self.lstm_layers:\n",
-    "            x, _ = lstm(x)\n",
-    "            x = self.dropout(x)\n",
-    "\n",
-    "        # Process through FC layers\n",
-    "        for fc in self.fc_layers:\n",
-    "            x = fc(x)\n",
-    "            x = self.dropout(x)\n",
-    "            x = torch.relu(x)\n",
-    "\n",
-    "        # Final projection\n",
-    "        out = self.final_layer(x)\n",
-    "        return out\n",
-    "\n",
-    "\n",
-    "model = MultilayerModel(\n",
-    "    params[\"vocab_size\"],\n",
-    "    params[\"embed_size\"],\n",
-    "    params[\"hidden_size\"],\n",
-    ")\n",
-    "model.to(params[\"device\"])\n",
-    "print(f\"Model created: {model.__class__.__name__}\")\n",
-    "optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n",
-    "print(f\"Optimizer: {optimizer.__class__.__name__}\")\n",
-    "criterion = nn.CrossEntropyLoss(\n",
-    "    ignore_index=-100\n",
-    ")  # Ignore the buffering index of -100 in the dataset\n",
-    "print(f\"Criterion: {criterion.__class__.__name__}\")"
+    "# Initialize metrics\n",
+    "metrics = TrainingMetrics(\n",
+    "    initial_loss=np.random.uniform(3, 7),\n",
+    "    initial_accuracy=0,\n",
+    "    noise_scale=params[\"noise_scale\"],\n",
+    "    loss_trend=params[\"loss_trend\"],\n",
+    "    accuracy_trend=params[\"accuracy_trend\"],\n",
+    ")"
    ]
   },
   {
@@ -279,7 +228,7 @@
     "from neptune_scale import Run\n",
     "\n",
     "run = Run(\n",
-    "    experiment_name=\"pytorch-text\",  # Create a run that is the head of an experiment.\n",
+    "    experiment_name=\"debugging-gradient-norms\",  # Create a run that is the head of an experiment.\n",
     ")\n",
     "\n",
     "print(run.get_experiment_url())"
@@ -300,15 +249,12 @@
    "source": [
     "run.log_configs(\n",
     "    {\n",
-    "        \"config/learning_rate\": params[\"learning_rate\"],\n",
-    "        \"config/optimizer\": params[\"optimizer\"],\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"data/embed_size\": params[\"embed_size\"],\n",
     "    }\n",
     ")\n",
     "\n",
-    "run.add_tags(tags=[\"text\", \"LLM\", \"multi-layer\", params[\"optimizer\"]])\n",
+    "run.add_tags(tags=[\"debug\", \"gradient-norm\"])\n",
     "\n",
     "print(f\"See configuration parameters:\\n{run.get_experiment_url() + '&detailsTab=metadata'}\")"
    ]
@@ -320,8 +266,8 @@
     "### Step 3: _Track gradient norms during training_\n",
     "\n",
     "In this training loop, we:\n",
-    "1. Register backward hooks to capture gradient norms from all model layers with `register_full_backward_hook()`\n",
-    "2. Track these norms during training to identify potential issues like vanishing/exploding gradients in a dictionary called `debugging_gradient_norms`\n",
+    "1. Simulate the calculation of `loss`, `accuracy` and `gradient norms`\n",
+    "2. Track gradient norms during training to identify potential issues like vanishing/exploding gradients in a dictionary called `gradient_norms`\n",
     "3. Log the gradient norms to Neptune for visualization and analysis using the `log_metrics` method\n",
     "\n",
     "This approach allows you to monitor the learning dynamics across your entire model architecture in near real-time."
@@ -333,45 +279,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Register hooks to track gradients for each layer\n",
-    "def hook_fn(module, grad_input, grad_output):\n",
-    "    layer_name = next(name for name, mod in model.named_modules() if mod is module)\n",
-    "    if grad_input[0] is not None:  # Check if gradients exist\n",
-    "        grad_norm = grad_input[0].norm().item()\n",
-    "        debugging_gradient_norms[f\"debug/gradient/{layer_name}/norm\"] = grad_norm\n",
-    "\n",
-    "\n",
-    "# Define dictionary of metrics to log to Neptune\n",
-    "debugging_gradient_norms = {}\n",
-    "# Register hooks once before training\n",
-    "for name, module in model.named_modules():\n",
-    "    module.register_full_backward_hook(hook_fn)\n",
-    "\n",
-    "# Create custom Neptune URLS for tutorial steps\n",
-    "print(f\"View charts in near real-time:\\n{run.get_experiment_url() + '&detailsTab=charts'}\")\n",
-    "\n",
     "step_counter = 0\n",
     "# Training loop\n",
-    "for epoch in range(params[\"epochs\"]):\n",
-    "    total_loss = 0\n",
-    "    for batch in train_dataloader:\n",
-    "        model.train()\n",
-    "        step_counter += 1\n",
-    "        input_ids = batch[\"input_ids\"].to(params[\"device\"])\n",
-    "        labels = batch[\"labels\"].to(params[\"device\"])\n",
-    "        optimizer.zero_grad()\n",
-    "        logits = model(input_ids)\n",
-    "        loss = criterion(logits.view(-1, params[\"vocab_size\"]), labels.view(-1))\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        # Log global training loss and layer-wise gradient norms\n",
+    "for epoch in range(1, params[\"epochs\"]):\n",
+    "    # Process batches\n",
+    "    for batch in range(0, 10000, params[\"batch_size\"]):\n",
+    "        # Update metrics for this batch\n",
+    "        batch_loss, batch_accuracy, gradient_norms = metrics.update_metrics()\n",
+    "\n",
     "        run.log_metrics(\n",
-    "            data={\"metrics/train/loss\": loss.item(), **debugging_gradient_norms},\n",
+    "            data={\n",
+    "                \"metrics/train/loss\": batch_loss,\n",
+    "                \"metrics/train/accuracy\": batch_accuracy,\n",
+    "                \"epoch\": epoch,\n",
+    "                **gradient_norms,\n",
+    "            },\n",
     "            step=step_counter,\n",
     "        )\n",
+    "        step_counter += 1\n",
     "\n",
-    "# Close run to ensure all operations are processed\n",
     "run.close()"
    ]
   },
@@ -389,7 +315,7 @@
     "\n",
     "**2. Advanced metric filtering**\n",
     "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/charts#filtering-charts)\n",
-    "- Example: `gradient & fc & layers.[0-5] & norm` filters gradients for the first 6 fully connected layers\n",
+    "- Example: `norm & layer_\\d` filter all gradient norms layers\n",
     "- Perfect for isolating problematic layers or components\n",
     "\n",
     "**3. Create custom dashboards**\n",
@@ -398,19 +324,14 @@
     "- Share with team members\n",
     "- Ideal for tracking known problematic layers\n",
     "\n",
-    "_Example dashboard:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=9e5a20fe-0810-4374-809c-02f55afe0548&dash=dashboard&dashboardId=9e43838c-2d76-4be0-a956-d8e6ad89ae23&compare=uJI-OW3tOzqHtPU9F8K_zIw4wtBWp0vj5uPHtuoXDVbA\">\n",
-    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
-    "</a>\n",
-    "\n",
     "**4. Dynamic metric analysis**\n",
-    "- Group related metrics (e.g., all LSTM layer gradients)\n",
+    "- Group related metrics (e.g., all layer gradients)\n",
     "- Create automatically updating charts by [selecting metrics dynamically](https://docs.neptune.ai/chart_widget/#dynamic-metric-selection)\n",
     "- Quickly identify vanishing/exploding gradients\n",
-    "- Example query: `(.*gradient)(.*lstm)(.*norm)`\n",
+    "- Example query: `layer_\\d`\n",
     "\n",
     "_Example dashboard:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=9e5a20fe-0810-4374-809c-02f55afe0548&dash=dashboard&dashboardId=9e43838c-2d76-4be0-a956-d8e6ad89ae23&compare=uJI-OW3tOzqHtPU9F8K_zIw4wtBWp0vj5uPHtuoXDVbA\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=standard-view&dash=dashboard&dashboardId=9e9e6760-9e80-433a-867f-1a7d1ff40d30&compare=u4ir2KsDA_hFibTp9gV7tFm8XD-q2pARCUx-tpoKiKFc&compareChartsFilter-compound=uYOMsIHr6vYTYKmC9EoPmJB2Ry6L9tcXGcV5uA99vgRg\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",

From 3e423f8e7af38748a417df4c64bc7cc10fad5ff7 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 2 May 2025 11:50:05 +0200
Subject: [PATCH 106/125] fix: add numpy to be installed as dependency

---
 .../debug-model-training-runs/debug_training_runs.ipynb         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 79b5857..4e4c429 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "# Install dependencies\n",
-    "! pip install -qU neptune_scale"
+    "! pip install -qU neptune_scale numpy"
    ]
   },
   {

From 90fa8d98adb860e7743e4fbbc5de12ed91a9da21 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 2 May 2025 15:00:13 +0200
Subject: [PATCH 107/125] chore: remove unneeded comments

---
 .../debug-model-training-runs/debug_training_runs.ipynb     | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 4e4c429..198ae26 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -156,10 +156,10 @@
     "            f\"debug/grad_norm/layer_{i}\": np.random.uniform(0.01, 0.1) for i in range(20)\n",
     "        }\n",
     "\n",
-    "        # Check for spike/anomaly (0.01% chance) after step 10k\n",
-    "        if spikes and np.random.random() < 0.0001:  # and self.step_count > 10000:\n",
+    "        # Check for spike/anomaly (0.01% chance)\n",
+    "        if spikes and np.random.random() < 0.0001:\n",
     "            # Generate a sudden spike, independent of current loss\n",
-    "            spike_magnitude = np.random.uniform(1, 10)  # Random spike between 1x and 5x\n",
+    "            spike_magnitude = np.random.uniform(1, 10)\n",
     "            current_loss = spike_magnitude\n",
     "\n",
     "            # When there's a loss spike, create a corresponding spike in a random layer\n",

From 7e4f3fff0f7f88415e1d239e18a2c0aa4e5a0890 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 22 May 2025 16:08:39 +0200
Subject: [PATCH 108/125] refactor: update model to PyTorch based

---
 .../debug_training_runs.ipynb                 | 259 +++++++++++-------
 1 file changed, 159 insertions(+), 100 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index 198ae26..cc229b5 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -96,104 +96,111 @@
    "source": [
     "### Setup training simulation\n",
     "\n",
-    "This tutorial uses a simulated training scenario to demonstrate Neptune's debugging capabilities. The simulation generates synthetic training metrics that mimic real model training, including:\n",
-    "- Gradually decreasing loss\n",
-    "- Increasing accuracy\n",
-    "- Random noise and occasional spikes to mimic gradient norm explosions\n",
-    "- Per-layer gradient norms that fluctuate naturally and spike during loss anomalies\n",
+    "This tutorial uses a simple PyTorch model trained on the MNIST dataset to demonstrate Neptune's debugging capabilities. The training code tracks real training metrics including:\n",
+    "- Loss values from the CrossEntropyLoss function\n",
+    "- Gradient norms for each layer to monitor optimization\n",
     "\n",
     "In the next cell, we'll:\n",
-    "1. Import required libraries (numpy, json, etc.) \n",
-    "2. Create a `TrainingMetrics` class that simulates training progression\n",
-    "3. Track gradient norms for 20 layers to help identify optimization issues\n",
+    "1. Import PyTorch libraries and utilities\n",
+    "2. Create a SimpleModel class with 20 layers\n",
+    "3. Add gradient norm tracking to identify potential issues during training\n",
+    "\n",
+    "The setup is not optimized for the best model, but for illustration of using Neptune for debugging the training runs that we create.\n",
     "\n",
     "_You can replace this simulation with your actual model training code._"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n# Create training step method\\ndef train_step(model, device, data, target, optimizer):\\n\\n    data, target = data.to(device), target.to(device)\\n    data = data.view(data.size(0), -1)  # Flatten the images\\n    \\n    # Forward pass\\n    optimizer.zero_grad()\\n    output = model(data)\\n    loss = criterion(output, target)\\n    \\n    # Backward pass\\n    loss.backward()\\n    \\n    # Get gradient norms for monitoring\\n    gradient_norms = model.get_gradient_norms()\\n    \\n    # Update weights\\n    optimizer.step()\\n    \\n    # Track statistics\\n    batch_loss = loss.item()'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "import numpy as np\n",
-    "import json\n",
-    "from typing import Dict, Any\n",
-    "import math\n",
-    "\n",
-    "\n",
-    "class TrainingMetrics:\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        initial_loss: float = 1.0,\n",
-    "        initial_accuracy: float = 0.0,\n",
-    "        noise_scale: float = 0.1,\n",
-    "        loss_trend: float = -0.1,\n",
-    "        accuracy_trend: float = 0.1,\n",
-    "    ):\n",
-    "        self.previous_loss = initial_loss\n",
-    "        self.previous_accuracy = initial_accuracy\n",
-    "        self.noise_scale = noise_scale\n",
-    "        self.loss_trend = loss_trend\n",
-    "        self.accuracy_trend = accuracy_trend\n",
-    "        self.step_count = 1\n",
-    "\n",
-    "        # Random convergence points\n",
-    "        self.target_loss = np.random.uniform(0.0, 1.0)\n",
-    "        self.target_accuracy = np.random.uniform(0.0, 1.0)\n",
-    "\n",
-    "    def update_metrics(self, spikes=True) -> tuple[float, float, dict]:\n",
-    "        \"\"\"Update loss and accuracy using a random walk process with logarithmic trends\"\"\"\n",
-    "        self.step_count += 1\n",
-    "\n",
-    "        # Base loss update with normal progression\n",
-    "        decay_factor = math.log(1 + abs(self.previous_loss - self.target_loss))\n",
-    "        loss_step = self.noise_scale * np.random.randn() + self.loss_trend * decay_factor\n",
-    "        loss_step *= 1 + 0.1 * math.log(self.step_count)\n",
-    "\n",
-    "        # Initialize gradient norms for 20 layers with small random values\n",
-    "        gradient_norms = {\n",
-    "            f\"debug/grad_norm/layer_{i}\": np.random.uniform(0.01, 0.1) for i in range(20)\n",
-    "        }\n",
-    "\n",
-    "        # Check for spike/anomaly (0.01% chance)\n",
-    "        if spikes and np.random.random() < 0.0001:\n",
-    "            # Generate a sudden spike, independent of current loss\n",
-    "            spike_magnitude = np.random.uniform(1, 10)\n",
-    "            current_loss = spike_magnitude\n",
-    "\n",
-    "            # When there's a loss spike, create a corresponding spike in a random layer\n",
-    "            spike_layer = np.random.randint(0, 20)\n",
-    "            gradient_norms[f\"debug/grad_norm/layer_{spike_layer}\"] = (\n",
-    "                spike_magnitude * np.random.uniform(0.8, 1.2)\n",
-    "            )\n",
-    "        else:\n",
-    "            # Normal progression\n",
-    "            current_loss = max(0.0, self.previous_loss + loss_step)\n",
-    "            self.previous_loss = current_loss\n",
-    "\n",
-    "        current_accuracy = 1 - current_loss\n",
-    "\n",
-    "        return current_loss, current_accuracy, gradient_norms\n",
-    "\n",
+    "# Pure PyTorch\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import time\n",
+    "\n",
+    "class SimpleModel(nn.Module):\n",
+    "    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):\n",
+    "        super().__init__()\n",
+    "        \n",
+    "        layers = []\n",
+    "        layers.append(nn.Linear(input_size, hidden_size))\n",
+    "        layers.append(nn.ReLU())\n",
+    "        \n",
+    "        for _ in range(18):\n",
+    "            layers.append(nn.Linear(hidden_size, hidden_size))\n",
+    "            layers.append(nn.ReLU())\n",
+    "\n",
+    "        layers.append(nn.Linear(hidden_size, output_size))\n",
+    "        \n",
+    "        # Combine all layers into a sequential model\n",
+    "        self.model = nn.Sequential(*layers)\n",
+    "        \n",
+    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+    "        return self.model(x)\n",
+    "    \n",
+    "    def get_gradient_norms(self) -> dict:\n",
+    "        \"\"\"\n",
+    "        Calculate the L2 norm of gradients for each layer.\n",
+    "        \n",
+    "        Returns:\n",
+    "            dict: Dictionary containing gradient norms for each layer\n",
+    "        \"\"\"\n",
+    "        gradient_norms = {}\n",
+    "        \n",
+    "        # Iterate through all named parameters\n",
+    "        for name, param in self.named_parameters():\n",
+    "            if param.grad is not None:\n",
+    "                # Calculate L2 norm of gradients\n",
+    "                norm = param.grad.norm(2).item()\n",
+    "                # Store in dictionary with a descriptive key\n",
+    "                gradient_norms[f\"debug/L2_grad_norm/{name}\"] = norm\n",
+    "                \n",
+    "        return gradient_norms\n",
+    "    \n",
     "\n",
     "# Training parameters\n",
     "params = {\n",
-    "    \"batch_size\": 32,\n",
-    "    \"epochs\": 100,\n",
-    "    \"noise_scale\": np.random.uniform(0.0005, 0.002),\n",
-    "    \"loss_trend\": -np.random.uniform(0, 0.0002),\n",
-    "    \"accuracy_trend\": np.random.uniform(0, 0.0002),\n",
+    "    \"batch_size\": 512,\n",
+    "    \"epochs\": 10,\n",
+    "    \"lr\": 0.001,\n",
+    "    \"num_layers\": 20,  # Configurable number of layers\n",
     "}\n",
     "\n",
-    "# Initialize metrics\n",
-    "metrics = TrainingMetrics(\n",
-    "    initial_loss=np.random.uniform(3, 7),\n",
-    "    initial_accuracy=0,\n",
-    "    noise_scale=params[\"noise_scale\"],\n",
-    "    loss_trend=params[\"loss_trend\"],\n",
-    "    accuracy_trend=params[\"accuracy_trend\"],\n",
-    ")"
+    "# Data transformations\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize((0.1307,), (0.3081,))\n",
+    "])\n",
+    "\n",
+    "# Load MNIST dataset\n",
+    "train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)\n",
+    "# test_dataset = datasets.MNIST('./data', train=False, transform=transform)\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
+    "# test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "# Initialize model, loss function, and optimizer\n",
+    "model = SimpleModel().to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=params[\"lr\"])"
    ]
   },
   {
@@ -221,9 +228,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=debugging-gradient-norms&type=experiment\n"
+     ]
+    }
+   ],
    "source": [
     "from neptune_scale import Run\n",
     "\n",
@@ -243,14 +258,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "See configuration parameters:\n",
+      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=debugging-gradient-norms&type=experiment&detailsTab=metadata\n"
+     ]
+    }
+   ],
    "source": [
     "run.log_configs(\n",
     "    {\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
+    "        \"config/lr\": params[\"lr\"]\n",
     "    }\n",
     ")\n",
     "\n",
@@ -266,31 +291,65 @@
     "### Step 3: _Track gradient norms during training_\n",
     "\n",
     "In this training loop, we:\n",
-    "1. Simulate the calculation of `loss`, `accuracy` and `gradient norms`\n",
+    "1. Calculate `loss` and ` L2 gradient norms` from the `named_parameters`.\n",
     "2. Track gradient norms during training to identify potential issues like vanishing/exploding gradients in a dictionary called `gradient_norms`\n",
     "3. Log the gradient norms to Neptune for visualization and analysis using the `log_metrics` method\n",
     "\n",
-    "This approach allows you to monitor the learning dynamics across your entire model architecture in near real-time."
+    "This approach allows you to monitor the learning dynamics across your entire model architecture in near real-time.\n",
+    "\n",
+    "_You can also use hooks to capture layer-wise training dynamics and log to Neptune_. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[32], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[0;32m      4\u001b[0m running_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[1;32m----> 6\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Move data to device\u001b[39;49;00m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Flatten the images\u001b[39;49;00m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\datasets\\mnist.py:146\u001b[0m, in \u001b[0;36mMNIST.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    143\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img\u001b[38;5;241m.\u001b[39mnumpy(), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 146\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    149\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:277\u001b[0m, in \u001b[0;36mNormalize.forward\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, tensor: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    270\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    271\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    272\u001b[0m \u001b[38;5;124;03m        tensor (Tensor): Tensor image to be normalized.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;124;03m        Tensor: Normalized Tensor image.\u001b[39;00m\n\u001b[0;32m    276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\functional.py:350\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensor, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg should be Tensor Image. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tensor)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\_functional_tensor.py:922\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    920\u001b[0m mean \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(mean, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m    921\u001b[0m std \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(std, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m--> 922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43m(\u001b[49m\u001b[43mstd\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43many\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    923\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstd evaluated to zero after conversion to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, leading to division by zero.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mean\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "step_counter = 0\n",
-    "# Training loop\n",
     "for epoch in range(1, params[\"epochs\"]):\n",
-    "    # Process batches\n",
-    "    for batch in range(0, 10000, params[\"batch_size\"]):\n",
-    "        # Update metrics for this batch\n",
-    "        batch_loss, batch_accuracy, gradient_norms = metrics.update_metrics()\n",
-    "\n",
+    "    model.train()\n",
+    "    running_loss = 0.0\n",
+    "    \n",
+    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
+    "        # Move data to device\n",
+    "        data, target = data.to(device), target.to(device)\n",
+    "        data = data.view(data.size(0), -1)  # Flatten the images\n",
+    "        \n",
+    "        optimizer.zero_grad()\n",
+    "        output = model(data)\n",
+    "        loss = criterion(output, target)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        gradient_norms = model.get_gradient_norms()\n",
+    "        batch_loss = loss.item()\n",
+    "        \n",
     "        run.log_metrics(\n",
     "            data={\n",
     "                \"metrics/train/loss\": batch_loss,\n",
-    "                \"metrics/train/accuracy\": batch_accuracy,\n",
     "                \"epoch\": epoch,\n",
     "                **gradient_norms,\n",
     "            },\n",
@@ -331,7 +390,7 @@
     "- Example query: `layer_\\d`\n",
     "\n",
     "_Example dashboard:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/compare?viewId=standard-view&dash=dashboard&dashboardId=9e9e6760-9e80-433a-867f-1a7d1ff40d30&compare=u4ir2KsDA_hFibTp9gV7tFm8XD-q2pARCUx-tpoKiKFc&compareChartsFilter-compound=uYOMsIHr6vYTYKmC9EoPmJB2Ry6L9tcXGcV5uA99vgRg\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?viewId=standard-view&detailsTab=dashboard&dashboardId=9e9e6760-9e80-433a-867f-1a7d1ff40d30&runIdentificationKey=PYTOR-444&type=run\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
@@ -342,7 +401,7 @@
     "- Share insights with team members\n",
     "\n",
     "_Example report:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9e79d952-272a-4a38-83e5-27df4dd225ec\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9ed051fc-9b16-4c31-a2da-925230c9c360\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",

From b0b3f564783dfc256f5f20db554794b29157a728 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 22 May 2025 16:21:53 +0200
Subject: [PATCH 109/125] chore: update readme and pre-commit accept

---
 README.md                                     |   5 +
 .../debug_training_runs.ipynb                 | 100 +++++-------------
 utils/migration_tools/from_wandb/README.md    |   2 +-
 3 files changed, 30 insertions(+), 77 deletions(-)

diff --git a/README.md b/README.md
index 2e0ddca..ff95c3b 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ This repo contains tutorials and examples of how to use Neptune.
 | Organize and filter runs        | [![docs-icon]][runs-table]   | [![neptune-icon]][runs-table-example]   |                                |                            |
 | Resume run or other object      | [![docs-icon]][resume-run]   |                                         |                                |                            |
 | Use Neptune in HPO jobs         | [![docs-icon]][hpo]          | [![neptune-icon]][hpo-example]          | [![github-icon]][hpo-notebook] | [![colab-icon]][hpo-colab] |
+| Debug training runs             | [![docs-icon]][debug]        | [![neptune-icon]][debug-example]        | [![github-icon]][debug-notebook] | [![colab-icon]][debug-colab] |
 
 ### Migration tools
 
@@ -43,6 +44,8 @@ This repo contains tutorials and examples of how to use Neptune.
 [hpo-colab]: https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/hpo/notebooks/Neptune_HPO.ipynb
 [qs-notebook]: how-to-guides/quickstart/notebooks/neptune_quickstart.ipynb
 [qs-colab]: https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/quickstart/notebooks/neptune_quickstart.ipynb
+[debug-notebook]: how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+[debug-colab]: https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
 
 <!-- External -->
 [blog]: https://neptune.ai/blog
@@ -58,6 +61,8 @@ This repo contains tutorials and examples of how to use Neptune.
 [resume-run]: https://docs.neptune.ai/resume_run
 [runs-table]: https://docs.neptune.ai/runs_table
 [runs-table-example]: https://scale.neptune.ai/o/examples/org/LLM-Pretraining/runs/table?viewId=9e746462-f045-4ff2-9ac4-e41fa349b04d&detailsTab=dashboard&dash=table&type=run&compare=auto-5
+[debug]: TODO - Add link to docs
+[debug-example]: TODO - Add link to final Neptune project "examples/debug-training-runs"
 
 <!-- Clickable icons -->
 [docs-icon]: https://neptune.ai/wp-content/uploads/2023/06/file_icon.svg "Read the documentation"
diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index cc229b5..ea6d619 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -112,20 +112,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n# Create training step method\\ndef train_step(model, device, data, target, optimizer):\\n\\n    data, target = data.to(device), target.to(device)\\n    data = data.view(data.size(0), -1)  # Flatten the images\\n    \\n    # Forward pass\\n    optimizer.zero_grad()\\n    output = model(data)\\n    loss = criterion(output, target)\\n    \\n    # Backward pass\\n    loss.backward()\\n    \\n    # Get gradient norms for monitoring\\n    gradient_norms = model.get_gradient_norms()\\n    \\n    # Update weights\\n    optimizer.step()\\n    \\n    # Track statistics\\n    batch_loss = loss.item()'"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Pure PyTorch\n",
     "import torch\n",
@@ -135,35 +124,36 @@
     "from torchvision import datasets, transforms\n",
     "import time\n",
     "\n",
+    "\n",
     "class SimpleModel(nn.Module):\n",
     "    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):\n",
     "        super().__init__()\n",
-    "        \n",
+    "\n",
     "        layers = []\n",
     "        layers.append(nn.Linear(input_size, hidden_size))\n",
     "        layers.append(nn.ReLU())\n",
-    "        \n",
+    "\n",
     "        for _ in range(18):\n",
     "            layers.append(nn.Linear(hidden_size, hidden_size))\n",
     "            layers.append(nn.ReLU())\n",
     "\n",
     "        layers.append(nn.Linear(hidden_size, output_size))\n",
-    "        \n",
+    "\n",
     "        # Combine all layers into a sequential model\n",
     "        self.model = nn.Sequential(*layers)\n",
-    "        \n",
+    "\n",
     "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
     "        return self.model(x)\n",
-    "    \n",
+    "\n",
     "    def get_gradient_norms(self) -> dict:\n",
     "        \"\"\"\n",
     "        Calculate the L2 norm of gradients for each layer.\n",
-    "        \n",
+    "\n",
     "        Returns:\n",
     "            dict: Dictionary containing gradient norms for each layer\n",
     "        \"\"\"\n",
     "        gradient_norms = {}\n",
-    "        \n",
+    "\n",
     "        # Iterate through all named parameters\n",
     "        for name, param in self.named_parameters():\n",
     "            if param.grad is not None:\n",
@@ -171,9 +161,9 @@
     "                norm = param.grad.norm(2).item()\n",
     "                # Store in dictionary with a descriptive key\n",
     "                gradient_norms[f\"debug/L2_grad_norm/{name}\"] = norm\n",
-    "                \n",
+    "\n",
     "        return gradient_norms\n",
-    "    \n",
+    "\n",
     "\n",
     "# Training parameters\n",
     "params = {\n",
@@ -184,13 +174,10 @@
     "}\n",
     "\n",
     "# Data transformations\n",
-    "transform = transforms.Compose([\n",
-    "    transforms.ToTensor(),\n",
-    "    transforms.Normalize((0.1307,), (0.3081,))\n",
-    "])\n",
+    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])\n",
     "\n",
     "# Load MNIST dataset\n",
-    "train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)\n",
+    "train_dataset = datasets.MNIST(\"./data\", train=True, download=True, transform=transform)\n",
     "# test_dataset = datasets.MNIST('./data', train=False, transform=transform)\n",
     "\n",
     "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
@@ -228,17 +215,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=debugging-gradient-norms&type=experiment\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from neptune_scale import Run\n",
     "\n",
@@ -258,24 +237,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "See configuration parameters:\n",
-      "https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?runIdentificationKey=debugging-gradient-norms&type=experiment&detailsTab=metadata\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "run.log_configs(\n",
     "    {\n",
     "        \"config/batch_size\": params[\"batch_size\"],\n",
     "        \"config/epochs\": params[\"epochs\"],\n",
-    "        \"config/lr\": params[\"lr\"]\n",
+    "        \"config/lr\": params[\"lr\"],\n",
     "    }\n",
     ")\n",
     "\n",
@@ -302,42 +272,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[32], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[0;32m      4\u001b[0m running_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[1;32m----> 6\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Move data to device\u001b[39;49;00m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Flatten the images\u001b[39;49;00m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    707\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    711\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m    712\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    713\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m    714\u001b[0m ):\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:764\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    763\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 764\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    766\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:52\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     54\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\datasets\\mnist.py:146\u001b[0m, in \u001b[0;36mMNIST.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    143\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img\u001b[38;5;241m.\u001b[39mnumpy(), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 146\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    149\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1737\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1739\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1748\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1749\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1750\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:277\u001b[0m, in \u001b[0;36mNormalize.forward\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, tensor: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m    270\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    271\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    272\u001b[0m \u001b[38;5;124;03m        tensor (Tensor): Tensor image to be normalized.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;124;03m        Tensor: Normalized Tensor image.\u001b[39;00m\n\u001b[0;32m    276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\functional.py:350\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensor, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg should be Tensor Image. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tensor)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\leo.breedt\\miniconda3\\envs\\neptune_scale_py_312_base\\Lib\\site-packages\\torchvision\\transforms\\_functional_tensor.py:922\u001b[0m, in \u001b[0;36mnormalize\u001b[1;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[0;32m    920\u001b[0m mean \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(mean, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m    921\u001b[0m std \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(std, dtype\u001b[38;5;241m=\u001b[39mdtype, device\u001b[38;5;241m=\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m--> 922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43m(\u001b[49m\u001b[43mstd\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43many\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    923\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstd evaluated to zero after conversion to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, leading to division by zero.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mean\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "step_counter = 0\n",
     "for epoch in range(1, params[\"epochs\"]):\n",
     "    model.train()\n",
     "    running_loss = 0.0\n",
-    "    \n",
+    "\n",
     "    for batch_idx, (data, target) in enumerate(train_loader):\n",
     "        # Move data to device\n",
     "        data, target = data.to(device), target.to(device)\n",
     "        data = data.view(data.size(0), -1)  # Flatten the images\n",
-    "        \n",
+    "\n",
     "        optimizer.zero_grad()\n",
     "        output = model(data)\n",
     "        loss = criterion(output, target)\n",
@@ -346,7 +294,7 @@
     "\n",
     "        gradient_norms = model.get_gradient_norms()\n",
     "        batch_loss = loss.item()\n",
-    "        \n",
+    "\n",
     "        run.log_metrics(\n",
     "            data={\n",
     "                \"metrics/train/loss\": batch_loss,\n",
diff --git a/utils/migration_tools/from_wandb/README.md b/utils/migration_tools/from_wandb/README.md
index 0fd6f03..6544294 100644
--- a/utils/migration_tools/from_wandb/README.md
+++ b/utils/migration_tools/from_wandb/README.md
@@ -78,4 +78,4 @@ See the License for the specific language governing permissions and limitations
 [docs-custom-views]: https://docs.neptune.ai/runs_table#custom-views
 [docs-project-access]: https://docs.neptune.ai/project_access
 [docs-reports]: https://docs.neptune.ai/reports/
-[docs-setup]: https://docs.neptune.ai/setup
\ No newline at end of file
+[docs-setup]: https://docs.neptune.ai/setup

From c35c43de15773df71988d2336e17f924b3cc0634 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 10:45:47 +0200
Subject: [PATCH 110/125] fix: add packages dependencies

---
 .../debug-model-training-runs/debug_training_runs.ipynb       | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
index ea6d619..982fbd9 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
@@ -77,7 +77,7 @@
    "source": [
     "### Install dependencies and import libraries\n",
     "\n",
-    "This tutorial uses a datset from Hugging Face which is loaded using the `datasets` package from Hugging Face."
+    "This tutorial uses PyTorch and the MNIST dataset for model training. "
    ]
   },
   {
@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "# Install dependencies\n",
-    "! pip install -qU neptune_scale numpy"
+    "! pip install -qU neptune_scale numpy torch torchvision"
    ]
   },
   {

From b7fe196b02e32886a978adf1adefb8e679dc2be6 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 11:13:38 +0200
Subject: [PATCH 111/125] refactor: update notebooks

---
 .../{ => notebooks}/debug_training_runs.ipynb              | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)
 rename how-to-guides/debug-model-training-runs/{ => notebooks}/debug_training_runs.ipynb (98%)

diff --git a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
similarity index 98%
rename from how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
rename to how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 982fbd9..6302598 100644
--- a/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Debug Model Training Runs with Neptune\n",
+    "# Log and Visualize Debugging Metrics in Neptune\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "# Install dependencies\n",
-    "! pip install -qU neptune_scale numpy torch torchvision"
+    "! pip install -qU neptune_scale torch torchvision"
    ]
   },
   {
@@ -116,14 +116,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Pure PyTorch\n",
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
     "from torch.utils.data import DataLoader\n",
     "from torchvision import datasets, transforms\n",
-    "import time\n",
-    "\n",
     "\n",
     "class SimpleModel(nn.Module):\n",
     "    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):\n",

From ebc9aee95101391c36b8c2c3ab1de24b676efef7 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 11:13:59 +0200
Subject: [PATCH 112/125] feat: add script version of tutorial

---
 .../scripts/debug_training_runs.py            | 134 ++++++++++++++++++
 .../scripts/requirements.txt                  |   3 +
 .../scripts/run_examples.sh                   |   7 +
 3 files changed, 144 insertions(+)
 create mode 100644 how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
 create mode 100644 how-to-guides/debug-model-training-runs/scripts/requirements.txt
 create mode 100644 how-to-guides/debug-model-training-runs/scripts/run_examples.sh

diff --git a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
new file mode 100644
index 0000000..17fb199
--- /dev/null
+++ b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
@@ -0,0 +1,134 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+from neptune_scale import Run
+
+class SimpleModel(nn.Module):
+    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):
+        super().__init__()
+
+        layers = []
+        layers.append(nn.Linear(input_size, hidden_size))
+        layers.append(nn.ReLU())
+
+        for _ in range(18):
+            layers.append(nn.Linear(hidden_size, hidden_size))
+            layers.append(nn.ReLU())
+
+        layers.append(nn.Linear(hidden_size, output_size))
+
+        # Combine all layers into a sequential model
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.model(x)
+
+    def get_gradient_norms(self) -> dict:
+        """
+        Calculate the L2 norm of gradients for each layer.
+
+        Returns:
+            dict: Dictionary containing gradient norms for each layer
+        """
+        gradient_norms = {}
+
+        # Iterate through all named parameters
+        for name, param in self.named_parameters():
+            if param.grad is not None:
+                # Calculate L2 norm of gradients
+                norm = param.grad.norm(2).item()
+                # Store in dictionary with a descriptive key
+                gradient_norms[f"debug/L2_grad_norm/{name}"] = norm
+
+        return gradient_norms
+
+def main():
+
+    # Training parameters
+    params = {
+        "batch_size": 512,
+        "epochs": 10,
+        "lr": 0.001,
+        "num_layers": 20,  # Configurable number of layers
+    }
+
+    # Data transformations
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+
+    # Load MNIST dataset
+    train_dataset = datasets.MNIST("./data", train=True, download=True, transform=transform)
+    # test_dataset = datasets.MNIST('./data', train=False, transform=transform)
+
+    train_loader = DataLoader(train_dataset, batch_size=params["batch_size"], shuffle=True)
+    # test_loader = DataLoader(test_dataset, batch_size=batch_size)
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    # Initialize model, loss function, and optimizer
+    model = SimpleModel().to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=params["lr"])
+
+
+    # Step 1: Initialize Neptune Run object
+    run = Run(
+        experiment_name="debugging-gradient-norms",  # Create a run that is the head of an experiment.
+    )
+
+    print(run.get_experiment_url())
+
+    # Step 2: Log configuration parameters
+    run.log_configs(
+        {
+            "config/batch_size": params["batch_size"],
+            "config/epochs": params["epochs"],
+            "config/lr": params["lr"],
+        }
+    )   
+
+    run.add_tags(tags=["debug", "gradient-norm"])
+
+    print(f"See configuration parameters:\n{run.get_experiment_url() + '&detailsTab=metadata'}")
+
+    # Step 3: Track gradient norms during training
+    step_counter = 0
+    for epoch in range(1, params["epochs"]):
+        model.train()
+        running_loss = 0.0
+
+        for batch_idx, (data, target) in enumerate(train_loader):
+            # Move data to device
+            data, target = data.to(device), target.to(device)
+            data = data.view(data.size(0), -1)  # Flatten the images
+
+            optimizer.zero_grad()
+            output = model(data)
+            loss = criterion(output, target)
+            loss.backward()
+            optimizer.step()
+
+            gradient_norms = model.get_gradient_norms()
+            batch_loss = loss.item()
+
+            run.log_metrics(
+                data={
+                    "metrics/train/loss": batch_loss,
+                    "epoch": epoch,
+                    **gradient_norms,
+                },
+                step=step_counter,
+            )
+            step_counter += 1
+
+    run.close()
+
+    # Step 4: Analyze training behavior
+    # While your model trains, use Neptune's web interface to monitor and analyze metrics in near real-time:
+    # 1. Real-time metric visualization
+    # 2. Advanced metric filtering
+    # 3. Create custom charts and dashboards
+    # 4. Dynamic metric analysis
+
+if __name__ == "__main__":
+    main()
diff --git a/how-to-guides/debug-model-training-runs/scripts/requirements.txt b/how-to-guides/debug-model-training-runs/scripts/requirements.txt
new file mode 100644
index 0000000..01c3673
--- /dev/null
+++ b/how-to-guides/debug-model-training-runs/scripts/requirements.txt
@@ -0,0 +1,3 @@
+neptune_scale
+torch 
+torchvision
diff --git a/how-to-guides/debug-model-training-runs/scripts/run_examples.sh b/how-to-guides/debug-model-training-runs/scripts/run_examples.sh
new file mode 100644
index 0000000..aa15b05
--- /dev/null
+++ b/how-to-guides/debug-model-training-runs/scripts/run_examples.sh
@@ -0,0 +1,7 @@
+set -e
+
+echo "Installing requirements..."
+pip install -Uq -r requirements.txt
+
+echo "Running debug_training_runs.py..."
+python debug_training_runs.py

From 75e88ad4a87455ca9471f7ce40974b032068d24f Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 11:16:26 +0200
Subject: [PATCH 113/125] chore: Add tests to GH workflows

---
 .github/workflows/test-notebooks.yml | 2 +-
 .github/workflows/test-scripts.yml   | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test-notebooks.yml b/.github/workflows/test-notebooks.yml
index 9207d9b..beb7391 100644
--- a/.github/workflows/test-notebooks.yml
+++ b/.github/workflows/test-notebooks.yml
@@ -20,8 +20,8 @@ jobs:
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
         notebooks: # Add in alphabetical order
+          - how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
           - how-to-guides/hpo/notebooks/Neptune_HPO.ipynb
-          - how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
           - how-to-guides/quickstart/notebooks/neptune_quickstart.ipynb
         os: ["${{ inputs.os }}"]
     steps:
diff --git a/.github/workflows/test-scripts.yml b/.github/workflows/test-scripts.yml
index 7a9344b..c30308a 100644
--- a/.github/workflows/test-scripts.yml
+++ b/.github/workflows/test-scripts.yml
@@ -20,6 +20,7 @@ jobs:
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
         scripts: # Add in alphabetical order
+          - how-to-guides/debug-model-training-runs/scripts
           - how-to-guides/hpo/scripts
           - how-to-guides/quickstart/scripts
         os: ["${{ inputs.os }}"]

From f0db1c2a95ed7ab02b2153af638cb2a58dec12da Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 11:22:17 +0200
Subject: [PATCH 114/125] fix: constrain numpy versions <2

---
 .../notebooks/debug_training_runs.ipynb                         | 2 +-
 .../debug-model-training-runs/scripts/requirements.txt          | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 6302598..b6e8dc4 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "# Install dependencies\n",
-    "! pip install -qU neptune_scale torch torchvision"
+    "! pip install -qU neptune_scale torch torchvision \"numpy<2\""
    ]
   },
   {
diff --git a/how-to-guides/debug-model-training-runs/scripts/requirements.txt b/how-to-guides/debug-model-training-runs/scripts/requirements.txt
index 01c3673..531d023 100644
--- a/how-to-guides/debug-model-training-runs/scripts/requirements.txt
+++ b/how-to-guides/debug-model-training-runs/scripts/requirements.txt
@@ -1,3 +1,4 @@
 neptune_scale
 torch 
 torchvision
+numpy<2
\ No newline at end of file

From d3c820060176b46297037a83d52a12ece93dfd21 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 11:57:37 +0200
Subject: [PATCH 115/125] chore: update links for colab and GH

---
 .../notebooks/debug_training_runs.ipynb               | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index b6e8dc4..1701ead 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -6,10 +6,10 @@
    "source": [
     "# Log and Visualize Debugging Metrics in Neptune\n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\"> \n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/main/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
     "</a>\n",
-    "<a target=\"_blank\" href=\"https://github.com/neptune-ai/scale-examples/blob/lb/debugging_model_training/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb\">\n",
+    "<a target=\"_blank\" href=\"https://github.com/neptune-ai/scale-examples/blob/main/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\">\n",
     "  <img alt=\"Open in GitHub\" src=\"https://img.shields.io/badge/Open_in_GitHub-blue?logo=github&labelColor=black\">\n",
     "</a>\n",
     "<a target=\"_blank\" href=\"https://docs-beta.neptune.ai/tutorials/\">\n",
@@ -348,12 +348,7 @@
     "_Example report:_\n",
     "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9ed051fc-9b16-4c31-a2da-925230c9c360\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
-    "</a>\n",
-    "\n",
-    "---\n",
-    "\n",
-    "**Additional resources:**\n",
-    "- [PyTorch Layer-wise Tracking Package](TODO:Link to integration for tracking layer-wise metrics)\n"
+    "</a>\n"
    ]
   }
  ],

From 4925f6956073f2e0c7dc5691e4e8c01f045fb946 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 14:48:52 +0200
Subject: [PATCH 116/125] chore: minor tweaks and update links to project

---
 .../notebooks/debug_training_runs.ipynb            | 14 ++++++++------
 .../scripts/debug_training_runs.py                 | 12 ++++++++----
 .../scripts/requirements.txt                       |  4 ++--
 3 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 1701ead..d1c7f95 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -32,7 +32,7 @@
     "3. Analyze the metrics in Neptune's UI to **debug training issues**\n",
     "\n",
     "Step through a pre-configured report:\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9ed051fc-9b16-4c31-a2da-925230c9c360\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
@@ -122,6 +122,7 @@
     "from torch.utils.data import DataLoader\n",
     "from torchvision import datasets, transforms\n",
     "\n",
+    "\n",
     "class SimpleModel(nn.Module):\n",
     "    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):\n",
     "        super().__init__()\n",
@@ -219,7 +220,8 @@
     "from neptune_scale import Run\n",
     "\n",
     "run = Run(\n",
-    "    experiment_name=\"debugging-gradient-norms\",  # Create a run that is the head of an experiment.\n",
+    "    experiment_name=\"debugging-gradient-norms\",\n",
+    "    project=\"examples/debug-training-metrics\",  # Create a run that is the head of an experiment.\n",
     ")\n",
     "\n",
     "print(run.get_experiment_url())"
@@ -319,7 +321,7 @@
     "\n",
     "**2. Advanced metric filtering**\n",
     "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/charts#filtering-charts)\n",
-    "- Example: `norm & layer_\\d` filter all gradient norms layers\n",
+    "- Example: `norm | \\d.weight` filter all gradient norms layers\n",
     "- Perfect for isolating problematic layers or components\n",
     "\n",
     "**3. Create custom dashboards**\n",
@@ -332,10 +334,10 @@
     "- Group related metrics (e.g., all layer gradients)\n",
     "- Create automatically updating charts by [selecting metrics dynamically](https://docs.neptune.ai/chart_widget/#dynamic-metric-selection)\n",
     "- Quickly identify vanishing/exploding gradients\n",
-    "- Example query: `layer_\\d`\n",
+    "- Example query: `\\d.weight$`\n",
     "\n",
     "_Example dashboard:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/runs/details?viewId=standard-view&detailsTab=dashboard&dashboardId=9e9e6760-9e80-433a-867f-1a7d1ff40d30&runIdentificationKey=PYTOR-444&type=run\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/compare?viewId=standard-view&dash=dashboard&dashboardId=9f0f002f-1852-448b-813d-b230de12b0b5&compare=uXbDuUl1H5I-cMeJj_n1iXy_roGF0tTL3IHVit0zU8Gs\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
     "\n",
@@ -346,7 +348,7 @@
     "- Share insights with team members\n",
     "\n",
     "_Example report:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/leo/pytorch-tutorial/reports/9ed051fc-9b16-4c31-a2da-925230c9c360\">\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n"
    ]
diff --git a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
index 17fb199..2703fb1 100644
--- a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
+++ b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
@@ -1,9 +1,10 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
+from neptune_scale import Run
 from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
-from neptune_scale import Run
+
 
 class SimpleModel(nn.Module):
     def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):
@@ -44,6 +45,7 @@ def get_gradient_norms(self) -> dict:
 
         return gradient_norms
 
+
 def main():
 
     # Training parameters
@@ -55,7 +57,9 @@ def main():
     }
 
     # Data transformations
-    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
 
     # Load MNIST dataset
     train_dataset = datasets.MNIST("./data", train=True, download=True, transform=transform)
@@ -70,7 +74,6 @@ def main():
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.Adam(model.parameters(), lr=params["lr"])
 
-
     # Step 1: Initialize Neptune Run object
     run = Run(
         experiment_name="debugging-gradient-norms",  # Create a run that is the head of an experiment.
@@ -85,7 +88,7 @@ def main():
             "config/epochs": params["epochs"],
             "config/lr": params["lr"],
         }
-    )   
+    )
 
     run.add_tags(tags=["debug", "gradient-norm"])
 
@@ -130,5 +133,6 @@ def main():
     # 3. Create custom charts and dashboards
     # 4. Dynamic metric analysis
 
+
 if __name__ == "__main__":
     main()
diff --git a/how-to-guides/debug-model-training-runs/scripts/requirements.txt b/how-to-guides/debug-model-training-runs/scripts/requirements.txt
index 531d023..eaa2f7d 100644
--- a/how-to-guides/debug-model-training-runs/scripts/requirements.txt
+++ b/how-to-guides/debug-model-training-runs/scripts/requirements.txt
@@ -1,4 +1,4 @@
 neptune_scale
-torch 
+numpy<2
+torch
 torchvision
-numpy<2
\ No newline at end of file

From 39aaa8116ff305aa1a4b03c4710d1a12f05b62f2 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 14:52:52 +0200
Subject: [PATCH 117/125] chore: readme links

---
 README.md                                                     | 4 ++--
 .../notebooks/debug_training_runs.ipynb                       | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index ff95c3b..c1434e2 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,7 @@ This repo contains tutorials and examples of how to use Neptune.
 [qs-notebook]: how-to-guides/quickstart/notebooks/neptune_quickstart.ipynb
 [qs-colab]: https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/quickstart/notebooks/neptune_quickstart.ipynb
 [debug-notebook]: how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
-[debug-colab]: https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/debug-model-training-runs/debug_training_runs.ipynb
+[debug-colab]: https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
 
 <!-- External -->
 [blog]: https://neptune.ai/blog
@@ -62,7 +62,7 @@ This repo contains tutorials and examples of how to use Neptune.
 [runs-table]: https://docs.neptune.ai/runs_table
 [runs-table-example]: https://scale.neptune.ai/o/examples/org/LLM-Pretraining/runs/table?viewId=9e746462-f045-4ff2-9ac4-e41fa349b04d&detailsTab=dashboard&dash=table&type=run&compare=auto-5
 [debug]: TODO - Add link to docs
-[debug-example]: TODO - Add link to final Neptune project "examples/debug-training-runs"
+[debug-example]: https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/table?viewId=standard-view&dash=table&compareChartsFilter-compound=udzSoRe3VmlvolZ8TbuB_zvfcAcgJmla8UuNku1rGWdg
 
 <!-- Clickable icons -->
 [docs-icon]: https://neptune.ai/wp-content/uploads/2023/06/file_icon.svg "Read the documentation"
diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index d1c7f95..28efa27 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Log and Visualize Debugging Metrics in Neptune\n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/main/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\"> \n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
     "</a>\n",
     "<a target=\"_blank\" href=\"https://github.com/neptune-ai/scale-examples/blob/main/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\">\n",

From d9d6e773cec7b87eef53ef4d62ed9097c6addd65 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Mon, 2 Jun 2025 15:10:17 +0200
Subject: [PATCH 118/125] small fixes

---
 .../notebooks/debug_training_runs.ipynb                     | 6 ++----
 .../scripts/debug_training_runs.py                          | 3 +--
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 28efa27..f160fa2 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -220,8 +220,7 @@
     "from neptune_scale import Run\n",
     "\n",
     "run = Run(\n",
-    "    experiment_name=\"debugging-gradient-norms\",\n",
-    "    project=\"examples/debug-training-metrics\",  # Create a run that is the head of an experiment.\n",
+    "    experiment_name=\"debugging-gradient-norms\", # Create a run that is the head of an experiment.\n",
     ")\n",
     "\n",
     "print(run.get_experiment_url())"
@@ -276,9 +275,8 @@
    "outputs": [],
    "source": [
     "step_counter = 0\n",
-    "for epoch in range(1, params[\"epochs\"]):\n",
+    "for epoch in range(params[\"epochs\"]):\n",
     "    model.train()\n",
-    "    running_loss = 0.0\n",
     "\n",
     "    for batch_idx, (data, target) in enumerate(train_loader):\n",
     "        # Move data to device\n",
diff --git a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
index 2703fb1..f918807 100644
--- a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
+++ b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
@@ -96,9 +96,8 @@ def main():
 
     # Step 3: Track gradient norms during training
     step_counter = 0
-    for epoch in range(1, params["epochs"]):
+    for epoch in range(params["epochs"]):
         model.train()
-        running_loss = 0.0
 
         for batch_idx, (data, target) in enumerate(train_loader):
             # Move data to device

From 0af184c1222f29b53d83fb9c780615951745590d Mon Sep 17 00:00:00 2001
From: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
Date: Fri, 6 Jun 2025 11:31:10 +0200
Subject: [PATCH 119/125] Apply suggestions from TW's review

Co-authored-by: Edyta <142720610+szaganek@users.noreply.github.com>
Signed-off-by: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
---
 README.md                                     |  2 +-
 .../notebooks/debug_training_runs.ipynb       | 56 +++++++++----------
 2 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/README.md b/README.md
index c1434e2..dfbcd11 100644
--- a/README.md
+++ b/README.md
@@ -61,7 +61,7 @@ This repo contains tutorials and examples of how to use Neptune.
 [resume-run]: https://docs.neptune.ai/resume_run
 [runs-table]: https://docs.neptune.ai/runs_table
 [runs-table-example]: https://scale.neptune.ai/o/examples/org/LLM-Pretraining/runs/table?viewId=9e746462-f045-4ff2-9ac4-e41fa349b04d&detailsTab=dashboard&dash=table&type=run&compare=auto-5
-[debug]: TODO - Add link to docs
+[debug]: https://docs.neptune.ai/debug_runs_tutorial
 [debug-example]: https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/table?viewId=standard-view&dash=table&compareChartsFilter-compound=udzSoRe3VmlvolZ8TbuB_zvfcAcgJmla8UuNku1rGWdg
 
 <!-- Clickable icons -->
diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index f160fa2..f1bef09 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Log and Visualize Debugging Metrics in Neptune\n",
+    "# Log and visualize debugging metrics in Neptune\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/neptune-ai/scale-examples/blob/master/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\"> \n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/> \n",
@@ -24,7 +24,7 @@
     "## Introduction\n",
     "Training large models requires careful monitoring of layer-wise metrics to catch issues early. \n",
     "\n",
-    "Neptune makes it easy to track and visualize metrics like gradient norms across all layers of your model - helping you identify problems like vanishing/exploding gradients quickly.\n",
+    "Neptune makes it easy to track and visualize metrics like gradient norms across all layers of your model – and helps you quickly identify problems such as vanishing or exploding gradients.\n",
     "\n",
     "In this tutorial, you'll learn how to:\n",
     "1. **Initialize Neptune** and **log configuration parameters**\n",
@@ -94,18 +94,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setup training simulation\n",
+    "### Set up training simulation\n",
     "\n",
     "This tutorial uses a simple PyTorch model trained on the MNIST dataset to demonstrate Neptune's debugging capabilities. The training code tracks real training metrics including:\n",
-    "- Loss values from the CrossEntropyLoss function\n",
+    "- Loss values from the `CrossEntropyLoss` function\n",
     "- Gradient norms for each layer to monitor optimization\n",
     "\n",
-    "In the next cell, we'll:\n",
-    "1. Import PyTorch libraries and utilities\n",
-    "2. Create a SimpleModel class with 20 layers\n",
-    "3. Add gradient norm tracking to identify potential issues during training\n",
+    "In the next cell, we will:\n",
+    "1. Import PyTorch libraries and utilities.\n",
+    "2. Create a SimpleModel class with 20 layers.\n",
+    "3. Add gradient norm tracking to identify potential issues during training.\n",
     "\n",
-    "The setup is not optimized for the best model, but for illustration of using Neptune for debugging the training runs that we create.\n",
+    "Note that this setup is not optimized for the best model, but for illustrating how to use Neptune for debugging the training runs that we create.\n",
     "\n",
     "_You can replace this simulation with your actual model training code._"
    ]
@@ -192,21 +192,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Debug model training run with Neptune\n",
+    "## Debug model training runs with Neptune\n",
     "\n",
-    "In this section, we'll walk through 4 key steps to effectively debug your training runs:\n",
+    "In this section, we'll walk through four key steps to effectively debug your training runs:\n",
     "\n",
-    "1. **Initialize Neptune** - set up the Neptune environment to track your training metrics\n",
-    "2. **Log configuration parameters** - record your model's hyperparameters and setup\n",
-    "3. **Track layer-wise metrics** - monitor gradient norms across all model layers\n",
-    "4. **Analyze training behavior** - Use Neptune's visualization tools to identify and diagnose training issues"
+    "1. **Initialize Neptune** – set up the Neptune environment to track your training metrics.\n",
+    "2. **Log configuration parameters** – record your model's hyperparameters and setup.\n",
+    "3. **Track layer-wise metrics** – monitor gradient norms across all model layers.\n",
+    "4. **Analyze training behavior** – Use Neptune's visualization tools to identify and diagnose training issues."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 1: _Initialize Neptune Run object_\n",
+    "### Step 1: Initialize Neptune Run object\n",
     "\n",
     "The `Run` object is used to log configuration parameters and metrics. "
    ]
@@ -220,7 +220,7 @@
     "from neptune_scale import Run\n",
     "\n",
     "run = Run(\n",
-    "    experiment_name=\"debugging-gradient-norms\", # Create a run that is the head of an experiment.\n",
+    "    experiment_name=\"debugging-gradient-norms\",  # Create a run that is the head of an experiment\n",
     ")\n",
     "\n",
     "print(run.get_experiment_url())"
@@ -230,7 +230,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 2: _Log configuration parameters_"
+    "### Step 2: Log configuration parameters"
    ]
   },
   {
@@ -256,12 +256,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 3: _Track gradient norms during training_\n",
+    "### Step 3: Track gradient norms during training\n",
     "\n",
-    "In this training loop, we:\n",
+    "In this training loop, we will:\n",
     "1. Calculate `loss` and ` L2 gradient norms` from the `named_parameters`.\n",
-    "2. Track gradient norms during training to identify potential issues like vanishing/exploding gradients in a dictionary called `gradient_norms`\n",
-    "3. Log the gradient norms to Neptune for visualization and analysis using the `log_metrics` method\n",
+    "2. Track gradient norms during training to identify potential issues like vanishing or exploding gradients in a dictionary called `gradient_norms`.\n",
+    "3. Use the `log_metrics` method to log the gradient norms to Neptune for visualization and analysis.\n",
     "\n",
     "This approach allows you to monitor the learning dynamics across your entire model architecture in near real-time.\n",
     "\n",
@@ -309,29 +309,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 4: _Analyze training behavior_\n",
+    "### Step 4: Analyze training behavior\n",
     "While your model trains, use Neptune's web interface to monitor and analyze metrics in near real-time:\n",
     "\n",
     "**1. Real-time metric visualization**\n",
-    "- Navigate to the _Charts_ tab to view live training metrics\n",
+    "- To view live training, navigate to the _Charts_ tab \n",
     "- Monitor multiple metrics simultaneously\n",
     "- Track training progress in real-time\n",
     "\n",
     "**2. Advanced metric filtering**\n",
     "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/charts#filtering-charts)\n",
-    "- Example: `norm | \\d.weight` filter all gradient norms layers\n",
+    "- Example: `norm | \\d.weight` filters all gradient norms layers\n",
     "- Perfect for isolating problematic layers or components\n",
     "\n",
     "**3. Create custom dashboards**\n",
     "- Save filtered metrics to a [custom dashboard](https://docs.neptune.ai/custom_dashboard) for continuous monitoring\n",
     "- Automatically updates during training\n",
-    "- Share with team members\n",
+    "- You can share dashboards with team members\n",
     "- Ideal for tracking known problematic layers\n",
     "\n",
     "**4. Dynamic metric analysis**\n",
-    "- Group related metrics (e.g., all layer gradients)\n",
+    "- Group related metric, for example all layer gradients\n",
     "- Create automatically updating charts by [selecting metrics dynamically](https://docs.neptune.ai/chart_widget/#dynamic-metric-selection)\n",
-    "- Quickly identify vanishing/exploding gradients\n",
+    "- Quickly identify vanishing or exploding gradients\n",
     "- Example query: `\\d.weight$`\n",
     "\n",
     "_Example dashboard:_\n",

From a1ef5e8b178069f4a636cac087cdaf9c0d4161ee Mon Sep 17 00:00:00 2001
From: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
Date: Fri, 6 Jun 2025 16:52:53 +0200
Subject: [PATCH 120/125] Apply suggestions from TW's review

Co-authored-by: Edyta <142720610+szaganek@users.noreply.github.com>
Signed-off-by: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
---
 .../notebooks/debug_training_runs.ipynb            | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index f1bef09..af55b00 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -27,11 +27,11 @@
     "Neptune makes it easy to track and visualize metrics like gradient norms across all layers of your model – and helps you quickly identify problems such as vanishing or exploding gradients.\n",
     "\n",
     "In this tutorial, you'll learn how to:\n",
-    "1. **Initialize Neptune** and **log configuration parameters**\n",
-    "2. Track **layer-wise gradient norms** during training \n",
-    "3. Analyze the metrics in Neptune's UI to **debug training issues**\n",
+    "1. **Initialize Neptune** and **log configuration parameters**.\n",
+    "2. Track **layer-wise gradient norms** during training.\n",
+    "3. Analyze the metrics in Neptune's UI to **debug training issues**.\n",
     "\n",
-    "Step through a pre-configured report:\n",
+    "See a pre-configured report in the Neptune app:\n",
     "<a target=\"_blank\" href=\"https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315\">\n",
     "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>\n",
@@ -102,7 +102,7 @@
     "\n",
     "In the next cell, we will:\n",
     "1. Import PyTorch libraries and utilities.\n",
-    "2. Create a SimpleModel class with 20 layers.\n",
+    "2. Create a `SimpleModel` class with 20 layers.\n",
     "3. Add gradient norm tracking to identify potential issues during training.\n",
     "\n",
     "Note that this setup is not optimized for the best model, but for illustrating how to use Neptune for debugging the training runs that we create.\n",
@@ -208,7 +208,7 @@
    "source": [
     "### Step 1: Initialize Neptune Run object\n",
     "\n",
-    "The `Run` object is used to log configuration parameters and metrics. "
+    "Use the `Run` object to log configuration parameters and metrics. "
    ]
   },
   {
@@ -259,7 +259,7 @@
     "### Step 3: Track gradient norms during training\n",
     "\n",
     "In this training loop, we will:\n",
-    "1. Calculate `loss` and ` L2 gradient norms` from the `named_parameters`.\n",
+    "1. Calculate `loss` and `L2 gradient norms` from the `named_parameters`.\n",
     "2. Track gradient norms during training to identify potential issues like vanishing or exploding gradients in a dictionary called `gradient_norms`.\n",
     "3. Use the `log_metrics` method to log the gradient norms to Neptune for visualization and analysis.\n",
     "\n",

From f3bae1a014a33b68582c3b80da9b8054da4df9dc Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Fri, 6 Jun 2025 17:19:43 +0200
Subject: [PATCH 121/125] refactor: updates from TW and sourcery review

---
 .../notebooks/debug_training_runs.ipynb        | 18 ++++++++++--------
 .../scripts/debug_training_runs.py             | 18 ++++++++++--------
 2 files changed, 20 insertions(+), 16 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index af55b00..522b2f6 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -124,14 +124,18 @@
     "\n",
     "\n",
     "class SimpleModel(nn.Module):\n",
-    "    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        input_size: int = 784,\n",
+    "        hidden_size: int = 128,\n",
+    "        output_size: int = 10,\n",
+    "        num_layers: int = 10,\n",
+    "    ):\n",
     "        super().__init__()\n",
     "\n",
-    "        layers = []\n",
-    "        layers.append(nn.Linear(input_size, hidden_size))\n",
-    "        layers.append(nn.ReLU())\n",
+    "        layers = [nn.Linear(input_size, hidden_size), nn.ReLU()]\n",
     "\n",
-    "        for _ in range(18):\n",
+    "        for _ in range(num_layers):\n",
     "            layers.append(nn.Linear(hidden_size, hidden_size))\n",
     "            layers.append(nn.ReLU())\n",
     "\n",
@@ -176,14 +180,12 @@
     "\n",
     "# Load MNIST dataset\n",
     "train_dataset = datasets.MNIST(\"./data\", train=True, download=True, transform=transform)\n",
-    "# test_dataset = datasets.MNIST('./data', train=False, transform=transform)\n",
     "\n",
     "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
-    "# test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
     "\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "# Initialize model, loss function, and optimizer\n",
-    "model = SimpleModel().to(device)\n",
+    "model = SimpleModel(num_layers=params[\"num_layers\"]).to(device)\n",
     "criterion = nn.CrossEntropyLoss()\n",
     "optimizer = optim.Adam(model.parameters(), lr=params[\"lr\"])"
    ]
diff --git a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
index f918807..6ed9ecd 100644
--- a/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
+++ b/how-to-guides/debug-model-training-runs/scripts/debug_training_runs.py
@@ -7,14 +7,18 @@
 
 
 class SimpleModel(nn.Module):
-    def __init__(self, input_size: int = 784, hidden_size: int = 128, output_size: int = 10):
+    def __init__(
+        self,
+        input_size: int = 784,
+        hidden_size: int = 128,
+        output_size: int = 10,
+        num_layers: int = 10,
+    ):
         super().__init__()
 
-        layers = []
-        layers.append(nn.Linear(input_size, hidden_size))
-        layers.append(nn.ReLU())
+        layers = [nn.Linear(input_size, hidden_size), nn.ReLU()]
 
-        for _ in range(18):
+        for _ in range(num_layers):
             layers.append(nn.Linear(hidden_size, hidden_size))
             layers.append(nn.ReLU())
 
@@ -63,14 +67,12 @@ def main():
 
     # Load MNIST dataset
     train_dataset = datasets.MNIST("./data", train=True, download=True, transform=transform)
-    # test_dataset = datasets.MNIST('./data', train=False, transform=transform)
 
     train_loader = DataLoader(train_dataset, batch_size=params["batch_size"], shuffle=True)
-    # test_loader = DataLoader(test_dataset, batch_size=batch_size)
 
     device = "cuda" if torch.cuda.is_available() else "cpu"
     # Initialize model, loss function, and optimizer
-    model = SimpleModel().to(device)
+    model = SimpleModel(num_layers=params["num_layers"]).to(device)
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.Adam(model.parameters(), lr=params["lr"])
 

From 6c828f239d270a41606d5cb3367fa8ba7567b73a Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Tue, 24 Jun 2025 16:18:14 +0200
Subject: [PATCH 122/125] refactor: update notebook workflow and links from TW
 review

---
 .../notebooks/debug_training_runs.ipynb       | 22 ++++++-------------
 1 file changed, 7 insertions(+), 15 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 522b2f6..4f9218b 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -14,6 +14,9 @@
     "</a>\n",
     "<a target=\"_blank\" href=\"https://docs-beta.neptune.ai/tutorials/\">\n",
     "  <img alt=\"View tutorial in docs\" src=\"https://neptune.ai/wp-content/uploads/2024/01/docs-badge-2.svg\">\n",
+    "</a>\n",
+    "<a target=\"_blank\" href=\"https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/table?viewId=standard-view\">\n",
+    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
     "</a>"
    ]
   },
@@ -31,10 +34,7 @@
     "2. Track **layer-wise gradient norms** during training.\n",
     "3. Analyze the metrics in Neptune's UI to **debug training issues**.\n",
     "\n",
-    "See a pre-configured report in the Neptune app:\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315\">\n",
-    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
-    "</a>\n",
+    "See a [pre-configured report](https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315) in the Neptune app.\n",
     "\n",
     "_Note: This is a code recipe that you can adapt for your own model training needs._"
    ]
@@ -223,9 +223,7 @@
     "\n",
     "run = Run(\n",
     "    experiment_name=\"debugging-gradient-norms\",  # Create a run that is the head of an experiment\n",
-    ")\n",
-    "\n",
-    "print(run.get_experiment_url())"
+    ")"
    ]
   },
   {
@@ -336,10 +334,7 @@
     "- Quickly identify vanishing or exploding gradients\n",
     "- Example query: `\\d.weight$`\n",
     "\n",
-    "_Example dashboard:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/compare?viewId=standard-view&dash=dashboard&dashboardId=9f0f002f-1852-448b-813d-b230de12b0b5&compare=uXbDuUl1H5I-cMeJj_n1iXy_roGF0tTL3IHVit0zU8Gs\">\n",
-    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
-    "</a>\n",
+    "[See an example dashboard](https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/compare?viewId=standard-view&dash=dashboard&dashboardId=9f0f002f-1852-448b-813d-b230de12b0b5&compare=uXbDuUl1H5I-cMeJj_n1iXy_roGF0tTL3IHVit0zU8Gs).\n",
     "\n",
     "**5. Document training insights**\n",
     "- Create [custom reports](https://docs.neptune.ai/reports) to track training progress\n",
@@ -347,10 +342,7 @@
     "- Maintain training history\n",
     "- Share insights with team members\n",
     "\n",
-    "_Example report:_\n",
-    "<a target=\"_blank\" href=\"https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315\">\n",
-    "  <img alt=\"Explore in Neptune\" src=\"https://neptune.ai/wp-content/uploads/2024/01/neptune-badge.svg\">\n",
-    "</a>\n"
+    "[See an example report](https://scale.neptune.ai/examples/debug-training-metrics/reports/Analyze-debugging-metrics-9f0f017e-c95a-4347-8bd4-2c50120f8315)."
    ]
   }
  ],

From 6b97205ffa75327fe8ae9712b7a13ff6b18ed0da Mon Sep 17 00:00:00 2001
From: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
Date: Thu, 26 Jun 2025 10:20:26 +0200
Subject: [PATCH 123/125] Apply suggestions from code review

Co-authored-by: Edyta <142720610+szaganek@users.noreply.github.com>
Signed-off-by: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
---
 .../notebooks/debug_training_runs.ipynb                       | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 4f9218b..7dcb10d 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -12,7 +12,7 @@
     "<a target=\"_blank\" href=\"https://github.com/neptune-ai/scale-examples/blob/main/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb\">\n",
     "  <img alt=\"Open in GitHub\" src=\"https://img.shields.io/badge/Open_in_GitHub-blue?logo=github&labelColor=black\">\n",
     "</a>\n",
-    "<a target=\"_blank\" href=\"https://docs-beta.neptune.ai/tutorials/\">\n",
+    "<a target=\"_blank\" href=\"https://docs.neptune.ai/tutorials/debug_runs\">\n",
     "  <img alt=\"View tutorial in docs\" src=\"https://neptune.ai/wp-content/uploads/2024/01/docs-badge-2.svg\">\n",
     "</a>\n",
     "<a target=\"_blank\" href=\"https://scale.neptune.ai/o/examples/org/debug-training-metrics/runs/table?viewId=standard-view\">\n",
@@ -318,7 +318,7 @@
     "- Track training progress in real-time\n",
     "\n",
     "**2. Advanced metric filtering**\n",
-    "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/charts#filtering-charts)\n",
+    "- Focus on specific metrics using [advanced regex search](https://docs.neptune.ai/regex)\n",
     "- Example: `norm | \\d.weight` filters all gradient norms layers\n",
     "- Perfect for isolating problematic layers or components\n",
     "\n",

From f19dfb7ea929315cfc5cc14fa0a028ac806da3c7 Mon Sep 17 00:00:00 2001
From: LeoRoccoBreedt <leo.breedt@neptune.ai>
Date: Thu, 26 Jun 2025 10:29:44 +0200
Subject: [PATCH 124/125] refactor: updates from TW's review

---
 .../notebooks/debug_training_runs.ipynb                         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 7dcb10d..57c3575 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -325,8 +325,8 @@
     "**3. Create custom dashboards**\n",
     "- Save filtered metrics to a [custom dashboard](https://docs.neptune.ai/custom_dashboard) for continuous monitoring\n",
     "- Automatically updates during training\n",
-    "- You can share dashboards with team members\n",
     "- Ideal for tracking known problematic layers\n",
+    "- You can share dashboards with team members\n",
     "\n",
     "**4. Dynamic metric analysis**\n",
     "- Group related metric, for example all layer gradients\n",

From 4abd1db95eeb242f75588efa6a1f70cacaef9bcc Mon Sep 17 00:00:00 2001
From: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
Date: Fri, 27 Jun 2025 16:34:35 +0200
Subject: [PATCH 125/125] Apply suggestions from code review

Remove reference to Scale

Co-authored-by: Edyta <142720610+szaganek@users.noreply.github.com>
Signed-off-by: Leo Breedt <101509998+LeoRoccoBreedt@users.noreply.github.com>
---
 .../notebooks/debug_training_runs.ipynb                   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
index 57c3575..2b9b6a4 100644
--- a/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
+++ b/how-to-guides/debug-model-training-runs/notebooks/debug_training_runs.ipynb
@@ -45,9 +45,9 @@
    "source": [
     "## Before you start\n",
     "\n",
-    "  1. Create a Neptune Scale account. [Register &rarr;](https://neptune.ai/early-access)\n",
-    "  2. Create a Neptune project for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune Scale docs.\n",
-    "  3. Install and configure Neptune Scale for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune Scale docs."
+    "  1. Create a Neptune account. [Register &rarr;](https://neptune.ai/early-access)\n",
+    "  2. Create a Neptune project for tracking metadata. For instructions, see [Projects](https://docs-beta.neptune.ai/projects/) in the Neptune docs.\n",
+    "  3. Install and configure Neptune for logging metadata. For instructions, see [Get started](https://docs-beta.neptune.ai/setup) in the Neptune docs."
    ]
   },
   {
@@ -55,7 +55,7 @@
    "metadata": {},
    "source": [
     "### Set environment variables\n",
-    "Set your project name and API token as environment variables to log to your Neptune Scale project.\n",
+    "Set your project name and API token as environment variables to log to your Neptune project.\n",
     "\n",
     "Uncomment the code block below and replace placeholder values with your own credentials:"
    ]