Updated notebooks with open in colab and default payloads

.gitignore

@@ -67,6 +67,7 @@ studio-pf.log
 
 # Credentials and secrets
 .geostudio_config_file
+geostudio_config_file
 *.env
 .env.local
 secrets.yml

workshop/docs/notebooks/lab1-getting-started.ipynb

@@ -7,6 +7,12 @@
    "source": [
     "# Lab 1\n",
     "\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/terrastackai/geospatial-studio/blob/main/workshop/docs/notebooks/lab1-getting-started.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\">\n",
+    "</a>\n",
+    "\n",
+    "\n",
     "**Getting Started with IBM Geospatial Studio**\n",
     "\n",
     "⏱️ **Estimated Duration:** 10 minutes  \n",
@@ -228,6 +234,8 @@
     "BASE_STUDIO_UI_URL=https://localhost:4180\n",
     "\"\"\"\n",
     "\n",
+    "# If you are using jupyter lab, create a normal file to view it on the fileviewer. `geostudio_config_file`\n",
+    "\n",
     "# Write to file\n",
     "with open('.geostudio_config_file', 'w') as f:\n",
     "    f.write(config_content)\n",

workshop/docs/notebooks/lab2-onboarding-examples.ipynb

@@ -7,6 +7,12 @@
    "source": [
     "# Lab 2\n",
     "\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/terrastackai/geospatial-studio/blob/main/workshop/docs/notebooks/lab2-onboarding-examples.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\">\n",
+    "</a>\n",
+    "\n",
+    "\n",
     "**Onboarding Pre-computed Examples**\n",
     "\n",
     "⏱️ **Estimated Duration:** 20 minutes  \n",
@@ -116,6 +122,8 @@
    "outputs": [],
    "source": [
     "# Initialize the client\n",
+    "# If you are using jupyter lab, create a normal file to view it on the fileviewer. `geostudio_config_file`\n",
+    "\n",
     "client = Client(geostudio_config_file=\".geostudio_config_file\")\n",
     "\n",
     "print(\"✅ Connected to Geospatial Studio!\")"
@@ -403,7 +411,7 @@
     "\n",
     "2. **Navigate to the Inference Lab:**\n",
     "   - Click on \"Inference Lab\" in the left sidebar\n",
-    "   - Or go directly to: `https://localhost:4180/inference`\n",
+    "   - Or go directly to: `https://localhost:4180/inference#inference`\n",
     "\n",
     "3. **Find Your Example:**\n",
     "   - Look for \"My Examples\" section\n",

workshop/docs/notebooks/lab3-running-inference.ipynb

@@ -7,6 +7,11 @@
    "source": [
     "# Lab 3\n",
     "\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/terrastackai/geospatial-studio/blob/main/workshop/docs/notebooks/lab3-running-inference.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\">\n",
+    "</a>\n",
+    "\n",
     "**Upload Model Checkpoints and Run Inference**\n",
     "\n",
     "⏱️ **Estimated Duration:** 30 minutes  \n",
@@ -132,6 +137,8 @@
    "outputs": [],
    "source": [
     "# Initialize the client\n",
+    "# If you are using jupyter lab, create a normal file to view it on the fileviewer. `geostudio_config_file`\n",
+    "\n",
     "client = Client(geostudio_config_file=\".geostudio_config_file\")\n",
     "\n",
     "print(\"✅ Connected to Geospatial Studio\")"

workshop/docs/notebooks/lab5-gesa-burnscars-workflow.ipynb

@@ -8,12 +8,12 @@
     "# Lab 5\n",
     "\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/terrastackai/geospatial-studio/blob/gesa-workshop-notebooks/workshop/docs/notebooks/lab5-gesa-burnscars-workflow.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\">\n",
+    "<a href=\"https://colab.research.google.com/github/terrastackai/geospatial-studio/blob/main/workshop/docs/notebooks/lab5-gesa-burnscars-workflow.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\">\n",
     "</a>\n",
     "\n",
     "\n",
-    "![Geospatial Studio banner](https://raw.githubusercontent.com/terrastackai/geospatial-studio/gesa-workshop-notebooks/workshop/docs/assets/banner.png)\n",
+    "![Geospatial Studio banner](https://raw.githubusercontent.com/terrastackai/geospatial-studio/main/workshop/docs/assets/banner.png)\n",
     "\n",
     "## 🌍 Geospatial Exploration and Orchestration Studio \n",
     "\n",
@@ -142,17 +142,17 @@
     "Raw satellite data showing the area affected by wildfire:\n",
     "\n",
     "<!-- ![Fire Input](../assets/fire-input.png) -->\n",
-    "<img src=\"https://raw.githubusercontent.com/terrastackai/geospatial-studio/gesa-workshop-notebooks/workshop/docs/assets/fire-input.png\" alt=\"RGB Input\" width=\"588\" height=\"300\">\n",
+    "<img src=\"https://raw.githubusercontent.com/terrastackai/geospatial-studio/main/workshop/docs/assets/fire-input.png\" alt=\"RGB Input\" width=\"588\" height=\"300\">\n",
     "\n",
     "#### Model Prediction\n",
     "The fine-tuned model identifies burn scar areas:\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/terrastackai/geospatial-studio/gesa-workshop-notebooks/workshop/docs/assets/fire-prediction.png\" alt=\"Fire Prediction\" width=\"588\" height=\"300\">\n",
+    "<img src=\"https://raw.githubusercontent.com/terrastackai/geospatial-studio/main/workshop/docs/assets/fire-prediction.png\" alt=\"Fire Prediction\" width=\"588\" height=\"300\">\n",
     "\n",
     "#### Prediction Overlay\n",
     "Burn scar predictions overlaid on the original imagery for validation:\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/terrastackai/geospatial-studio/gesa-workshop-notebooks/workshop/docs/assets/fire-prediction-overlayed.png\" alt=\"Fire Prediction Overlayed\" width=\"588\" height=\"300\">\n",
+    "<img src=\"https://raw.githubusercontent.com/terrastackai/geospatial-studio/main/workshop/docs/assets/fire-prediction-overlayed.png\" alt=\"Fire Prediction Overlayed\" width=\"588\" height=\"300\">\n",
     "\n",
     "<!-- ![Fire Prediction Overlayed](../assets/fire-prediction-overlayed.png) -->\n",
     "\n",
@@ -259,7 +259,9 @@
     "#############################################################\n",
     "# Initialize Geostudio client using a geostudio config file\n",
     "#############################################################\n",
-    "gfm_client = Client(geostudio_config_file=\".geostudio_config_file\")"
+    "# If you are using jupyter lab, create a normal file to view it on the fileviewer. `geostudio_config_file`\n",
+    "\n",
+    "gfm_client = Client(geostudio_config_file=\"geostudio_config_file\")"
    ]
   },
   {
@@ -280,12 +282,6 @@
     "In this section, we'll explore how to review datasets that have already been prepared and onboarded to the Geospatial Studio. This is useful for understanding what data is available before starting a fine-tuning task."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ae7d34b9",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -309,10 +305,16 @@
    "source": [
     "# Get detailed information about a specific burn scars dataset\n",
     "# If you know the dataset name or ID, you can retrieve its details\n",
-    "burn_scars_dataset_id = \"geodata-e8hwqfrvtsgwfgsx7agvgk\"\n",
+    "burn_scars_dataset_id = \"select-dataset_id-from-DataFrame-above\"\n",
     "burn_scars_dataset_id"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "31fee87f",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -381,10 +383,18 @@
    "source": [
     "# List all the base models onboarded in the cluster\n",
     "base_models = gfm_client.list_base_models()['results']\n",
-    "display(DataFrame(base_models))\n",
-    "\n",
+    "display(DataFrame(base_models))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06dfa760",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Select Prithvi_EO_V2_300M model\n",
-    "prithvi_eo_v2_300m_base_model_id = \"28ad8b4f-13ba-4567-b28f-bb16758324c3\""
+    "prithvi_eo_v2_300m_base_model_id = \"select-base_model-id-from-DataFrame-above\""
    ]
   },
   {
@@ -429,10 +439,18 @@
    "source": [
     "# List all the templates onboarded in the cluster\n",
     "tune_templates = gfm_client.list_tune_templates()['results']\n",
-    "display(DataFrame(tune_templates))\n",
-    "\n",
+    "display(DataFrame(tune_templates))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aecd8db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Select segmentation template\n",
-    "segmentation_template_id = \"8573acf1-5ee7-4291-bcfe-357408080445\""
+    "segmentation_template_id = \"select-template-id-from-DataFrame-above\""
    ]
   },
   {
@@ -512,6 +530,8 @@
     "5. **Completion**: Finalizing and storing the trained model\n",
     "\n",
     "**Expected Duration:**\n",
+    "\n",
+    "These times are dependent on the tuning config e.g. number of epochs, batch size, dataset size, etc.\n",
     "- Modern GPU (V100/A100): 30-45 minutes\n",
     "- Older GPU: 45-90 minutes\n",
     "- CPU: Not recommended (2 hrs + Using Terramind tiny )\n",
@@ -727,7 +747,7 @@
     "            \"visible_by_default\": \"True\"\n",
     "        }\n",
     "    ],\n",
-    "    \"tune_id\" : \"geotune-k9xhhztv6rqub8usa4xjdp\"\n",
+    "    \"tune_id\" : tune_id\n",
     "}\n",
     "\n",
     "print(\"📋 Inference Configuration:\")\n",
@@ -745,7 +765,6 @@
    "source": [
     "# Submit the inference request\n",
     "print(\"\\n🚀 Submitting inference request...\\n\")\n",
-    "tune_id = \"geotune-k9xhhztv6rqub8usa4xjdp\"\n",
     "\n",
     "inference_response = gfm_client.try_out_tune(tune_id=tune_id, data=inference_payload)\n",
     "\n",
@@ -824,20 +843,105 @@
    "outputs": [],
    "source": [
     "# The best model already onboarded in the cluster\n",
-    "best_burnscars_model = \"geotune-mgmshlc8b9mwksedrghray\"\n",
+    "best_burnscars_model = \"replace-with-best-burnscars_model_id\" \n",
     "# Run an inference on the best model\n",
-    "gfm_client.try_out_tune(tune_id=tune_id, data={**inference_payload, \"description\": \"Best Model Inference\", \"tune_id\":best_burnscars_model})\n",
+    "gfm_client.try_out_tune(tune_id=tune_id, data={**inference_payload, \"description\": \"Burns Best Model Inference\", \"tune_id\":best_burnscars_model})\n",
     "\n",
     "\n",
     "# Use 5.1 instructions to view the results in the UI"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "44a8b962",
+   "metadata": {},
+   "source": [
+    "## 7.0 Download artifacts : Download our trained model artifacts\n",
+    "\n",
+    "With the trained model, we will download the checkpoint and config that we can re-use for running inferences. \n",
+    "\n",
+    "We will use wget/curl to download the artifacts. Otherwise, you can grab the links and download directly to your local machine.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33e73e49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the download URLs for the trained model artifacts\n",
+    "artifact_urls = gfm_client.download_tune(tune_id=tune_id)\n",
+    "artifact_urls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "download-artifacts",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract URLs\n",
+    "config_url = artifact_urls['config_url']\n",
+    "checkpoint_url = artifact_urls['checkpoint_url']\n",
+    "\n",
+    "print(f\"Config URL: {config_url}\")\n",
+    "print(f\"Checkpoint URL: {checkpoint_url}\")\n",
+    "print(\"\\n--- Download Commands ---\\n\")\n",
+    "\n",
+    "# Using wget\n",
+    "print(\"Using wget:\")\n",
+    "print(f\"wget -O config_deploy.yaml '{config_url}'\")\n",
+    "print(f\"wget -O model_checkpoint.ckpt '{checkpoint_url}'\")\n",
+    "\n",
+    "# Using wget\n",
+    "print(\"\\nUsing curl:\")\n",
+    "print(f\"curl -o config_deploy.yaml '{config_url}'\")\n",
+    "print(f\"curl -o model_checkpoint.ckpt '{checkpoint_url}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5339feab",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 7.1 Extra: Upload Completed tune artifacts\n",
+    "For models tuned outside the GEOStudio platform, you can upload the model artifacts and use them to ran inferences.\n",
+    "\n",
+    "\n",
+    "```py\n",
+    "\n",
+    "burns_json = {\n",
+    "    \"name\": \"name_of_your_tune\",\n",
+    "    \"description\": \"name_of_your_tune\",\n",
+    "    \"tune_config_url\": \"https://url-to-download-your-config/your-tune-config.yaml\",\n",
+    "    \"tune_checkpoint_url\": \"https://url-to-download-your-checkpoint/your-checkpoint.ckpt\",\n",
+    "    # These are optional and can be updated when running the inference\n",
+    "    \"model_input_data_spec\": {\n",
+    "        \"bands\": [],  # bands used to train your dataset\n",
+    "        \"connector\": \"your_terrakit_connector\",\n",
+    "        \"collection\": \"the_collection_name\",\n",
+    "        \"file_suffix\": \"file_suffix_of_your_train_dataset\",\n",
+    "    },\n",
+    "    \"geoserver_push\": {},  # geoserver layers\n",
+    "    \"post_processing\": {},  # post_processing options\n",
+    "}\n",
+    "\n",
+    "tune_response = gfm.upload_completed_tunes(burns_json)\n",
+    "\n",
+    "___"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "summary-section-2",
    "metadata": {},
    "source": [
-    "## 7. Summary and Key Takeaways\n",
+    "## 8. Summary and Key Takeaways\n",
     "\n",
     "Congratulations! You've completed an end-to-end machine learning workflow for geospatial AI with a burnscars example. 🎉\n",
     "\n",