feat: Remove logging gradients by default (#131)

* refactor: Disable logging gradients in default callbacks

* build: Upgrade version, update changelog

* refactor: Remove gradient logging from default callback, add automatic batch size callback to all callback

* docs: Update changelog

Author: lorenzomammana

CHANGELOG.md

@@ -2,6 +2,13 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+### [2.2.6]
+
+#### Updated
+
+- Remove gradients logging callback from default configs to avoid slowing down the training process
+- Add automatic batch size calculator as default in `all` callback configuration
+
 ### [2.2.5]
 
 #### Updated

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "quadra"
-version = "2.2.5"
+version = "2.2.6"
 description = "Deep Learning experiment orchestration library"
 authors = [
 	"Federico Belotti <[email protected]>",

quadra/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.2.5"
+__version__ = "2.2.6"
 
 
 def get_version():

quadra/configs/callbacks/all.yaml

@@ -30,3 +30,16 @@ progress_bar:
 lightning_trainer_setup:
   _target_: quadra.callbacks.lightning.LightningTrainerBaseSetup
   log_every_n_steps: 1
+
+batch_size_finder:
+  _target_: quadra.callbacks.lightning.BatchSizeFinder
+  mode: power
+  steps_per_trial: 3
+  init_val: 2
+  max_trials: 5 # Max 64
+  batch_arg_name: batch_size
+  disable: false
+  find_train_batch_size: true
+  find_validation_batch_size: false
+  find_test_batch_size: false
+  find_predict_batch_size: false

quadra/configs/callbacks/default.yaml

@@ -9,9 +9,6 @@ model_checkpoint:
   filename: "epoch_{epoch:03d}"
   auto_insert_metric_name: False
 
-log_gradients:
-  _target_: quadra.callbacks.mlflow.LogGradients
-  norm: 2
 lr_monitor:
   _target_: pytorch_lightning.callbacks.LearningRateMonitor
   logging_interval: "epoch"

quadra/configs/callbacks/default_anomalib.yaml

@@ -44,9 +44,6 @@ upload_ckpts_as_artifact:
   upload_best_only: true
   delete_after_upload: true
   upload: false
-log_gradients:
-  _target_: quadra.callbacks.mlflow.LogGradients
-  norm: 2
 lr_monitor:
   _target_: pytorch_lightning.callbacks.LearningRateMonitor
   logging_interval: "epoch"