feat(config) Update default implementations of configurations

configs/__init__.py

@@ -1 +0,0 @@
-# this file is needed here to include configs when building project as a package

configs/callbacks/default.yaml

@@ -1,22 +1,17 @@
 defaults:
-  - model_checkpoint
-  - early_stopping
-  - model_summary
-  - rich_progress_bar
+  - model_checkpoint # checkpointing
+  - rich_model_summary # model summary
+  - tqdm_progress_bar # progress bar
   - _self_
 
+# Overrides the default configurations
 model_checkpoint:
   dirpath: ${paths.output_dir}/checkpoints
-  filename: "epoch_{epoch:03d}"
-  monitor: "val/acc"
-  mode: "max"
-  save_last: True
-  auto_insert_metric_name: False
-
-early_stopping:
-  monitor: "val/acc"
-  patience: 100
-  mode: "max"
+  filename: "epoch_{epoch:03d}_${oc.env:PROJECT_GIT_SHORT_SHA}"
+  monitor: "val/loss"
+  mode: "min"
+  save_last: true
+  auto_insert_metric_name: false
 
 model_summary:
-  max_depth: -1
+  max_depth: 2

configs/callbacks/early_stopping.yaml

@@ -1,9 +1,10 @@
-# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html
+# See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.pytorch.callbacks.EarlyStopping
 
 early_stopping:
   _target_: lightning.pytorch.callbacks.EarlyStopping
+  _convert_: "all"
   monitor: ??? # quantity to be monitored, must be specified !!!
-  min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
+  min_delta: 0.0 # minimum change in the monitored quantity to qualify as an improvement
   patience: 3 # number of checks with no improvement after which training will be stopped
   verbose: False # verbosity mode
   mode: "min" # "max" means higher metric value is better, can be also "min"
@@ -12,4 +13,4 @@ early_stopping:
   stopping_threshold: null # stop training immediately once the monitored quantity reaches this threshold
   divergence_threshold: null # stop training as soon as the monitored quantity becomes worse than this threshold
   check_on_train_epoch_end: null # whether to run early stopping at the end of the training epoch
-  # log_rank_zero_only: False  # this keyword argument isn't available in stable version
+  log_rank_zero_only: False # this keyword argument isn't available in stable version

configs/callbacks/model_checkpoint.yaml

@@ -1,7 +1,8 @@
-# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
+# See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#lightning.pytorch.callbacks.ModelCheckpoint
 
 model_checkpoint:
   _target_: lightning.pytorch.callbacks.ModelCheckpoint
+  _convert_: "all"
   dirpath: null # directory to save the model file
   filename: null # checkpoint filename
   monitor: null # name of the logged metric which determines when model is improving

configs/callbacks/model_summary.yaml

@@ -1,5 +1,6 @@
-# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html
+# See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelSummary.html
 
 model_summary:
-  _target_: lightning.pytorch.callbacks.RichModelSummary
-  max_depth: 1 # the maximum depth of layer nesting that the summary will include
+  _target_: lightning.pytorch.callbacks.ModelSummary
+  _convert_: "all"
+  max_depth: 1

configs/callbacks/rich_model_summary.yaml

@@ -0,0 +1,6 @@
+# See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html?highlight=richmodelsummary#lightning.pytorch.callbacks.RichModelSummary
+
+model_summary:
+  _target_: lightning.pytorch.callbacks.RichModelSummary
+  _convert_: "all"
+  max_depth: 1

configs/callbacks/rich_progress_bar.yaml

@@ -1,4 +1,20 @@
-# https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html
+# See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichProgressBar.html?highlight=progressbar#lightning.pytorch.callbacks.RichProgressBar
 
 rich_progress_bar:
   _target_: lightning.pytorch.callbacks.RichProgressBar
+  _convert_: "all"
+  refresh_rate: 1 # int: Determines at which rate (in number of batches) the progress bars get updated. Set it to 0 to disable the display.
+  leave: false # bool: Leaves the finished progress bar in the terminal at the end of the epoch. Default: False
+  theme: # RichProgressBarTheme: Contains styles used to stylize the progress bar.
+    # See https://lightning.ai/docs/pytorch/stable/_modules/lightning/pytorch/callbacks/progress/rich_progress.html#RichProgressBarTheme
+    _target_: lightning.pytorch.callbacks.progress.rich_progress.RichProgressBarTheme
+    _convert_: "all"
+    description: "Steps" # str: Description of the progress bar.
+    progress_bar: "#CFB991" # str: Style for the bar in progress.
+    progress_bar_finished: "#A0C544" # str: Style for the finished progress bar.
+    progress_bar_pulse: "#555960" # str: Style for the progress bar when `IterableDataset` is being processed.
+    batch_progress: "white" # str: Style for the progress tracker (i.e 10/50 batches completed).
+    time: "grey54" # str: Style for the processed time and estimate time remaining
+    processing_speed: "grey70" # str: Style for the speed of the batches being processed
+    metrics: "white" # str: Style for the metrics
+  console_kwargs: null # Optional[Dict[str, Any]]: Args for constructing a Console

configs/callbacks/tqdm_progress_bar.yaml

@@ -0,0 +1,20 @@
+# See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichProgressBar.html?highlight=progressbar#lightning.pytorch.callbacks.RichProgressBar
+
+rich_progress_bar:
+  _target_: lightning.pytorch.callbacks.RichProgressBar
+  _convert_: "all"
+  refresh_rate: 1 # int: Determines at which rate (in number of batches) the progress bars get updated. Set it to 0 to disable the display.
+  leave: false # bool: Leaves the finished progress bar in the terminal at the end of the epoch. Default: False
+  theme: # RichProgressBarTheme: Contains styles used to stylize the progress bar.
+    # See https://lightning.ai/docs/pytorch/stable/_modules/lightning/pytorch/callbacks/progress/rich_progress.html#RichProgressBarTheme
+    _target_: lightning.pytorch.callbacks.progress.rich_progress.RichProgressBarTheme
+    _convert_: "all"
+    description: "Steps" # str: Description of the progress bar.
+    progress_bar: "#CFB991" # str: Style for the bar in progress.
+    progress_bar_finished: "#A0C544" # str: Style for the finished progress bar.
+    progress_bar_pulse: "#555960" # str: Style for the progress bar when `IterableDataset` is being processed.
+    batch_progress: "white" # str: Style for the progress tracker (i.e 10/50 batches completed).
+    time: "grey54" # str: Style for the processed time and estimate time remaining
+    processing_speed: "grey70" # str: Style for the speed of the batches being processed
+    metrics: "white" # str: Style for the metrics
+  console_kwargs: null # Optional[Dict[str, Any]]: Args for constructing a Console

configs/eval.yaml

@@ -1,18 +1,34 @@
 # @package _global_
-
 defaults:
   - _self_
-  - data: mnist # choose datamodule with `test_dataloader()` for evaluation
-  - model: mnist
-  - logger: null
+  - dataset: null
+  - model: null
+  - callbacks: default
+  - logger: tensorboard
   - trainer: default
   - paths: default
   - extras: default
   - hydra: default
+  # Experiment specific settings
+  - experiment: null
+
+# Enforce clean git repository
+clean_repo: false
 
+# Job name
 task_name: "eval"
 
-tags: ["dev"]
+# Experiment tags
+tags: "dev"
+
+# If true, train the model from scratch
+train: false
+
+# If to compile the model for acceleration
+compile: false
+
+# Specify the previous checkpoint to resume training
+checkpoint: null
 
-# passing checkpoint path is necessary for evaluation
-ckpt_path: ???
+# Global seed
+seed: 42

configs/extras/default.yaml

@@ -1,8 +1,8 @@
-# disable python warnings if they annoy you
-ignore_warnings: False
+# disable Python warnings
+ignore_warnings: true
 
-# ask user for tags if none are provided in the config
-enforce_tags: True
+# If true, enforce tags for experiment tracking
+enforce_tags: true
 
-# pretty print config tree at the start of the run using Rich library
-print_config: True
+# pretty print the configuration tree before training
+print_config: true

configs/hydra/default.yaml

@@ -1,19 +1,12 @@
-# https://hydra.cc/docs/configure_hydra/intro/
-
-# enable color logging
 defaults:
+  # enable color logging
   - override hydra_logging: colorlog
   - override job_logging: colorlog
 
-# output directory, generated dynamically on each run
+# Output directory
 run:
-  dir: ${paths.log_dir}/${task_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S}
+  # directory for a single run
+  dir: ${paths.log_dir}/${task_name}/output/${join_string_underscore:${tags}}/${now:%Y-%m-%d_%H-%M-%S}
 sweep:
-  dir: ${paths.log_dir}/${task_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S}
-  subdir: ${hydra.job.num}
-
-job_logging:
-  handlers:
-    file:
-      # Incorporates fix from https://github.com/facebookresearch/hydra/pull/2242
-      filename: ${hydra.runtime.output_dir}/${task_name}.log
+  # directory for a queue of sub-runs
+  dir: ${paths.log_dir}/${task_name}/output/multiruns/${join_string_underscore:${tags}}/${now:%Y-%m-%d_%H-%M-%S}