Initial commit

configs/callbacks/default.yaml

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+defaults:
+  - model_checkpoint.yaml
+  - model_summary.yaml
+  - rich_progress_bar.yaml
+  - _self_

configs/callbacks/model_checkpoint.yaml

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+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
+
+model_checkpoint:
+  _target_: lightning.pytorch.callbacks.ModelCheckpoint
+  dirpath: ${paths.output_dir}/checkpoints # directory to save the model file
+  filename: checkpoint_{epoch:03d}  # checkpoint filename
+  monitor: epoch # name of the logged metric which determines when model is improving
+  verbose: False # verbosity mode
+  save_last: true # additionally always save an exact copy of the last checkpoint to a file last.ckpt
+  save_top_k: 10 # save k best models (determined by above metric)
+  mode: "max" # "max" means higher metric value is better, can be also "min"
+  auto_insert_metric_name: True # when True, the checkpoints filenames will contain the metric name
+  save_weights_only: False # if True, then only the model’s weights will be saved
+  every_n_train_steps: null # number of training steps between checkpoints
+  train_time_interval: null # checkpoints are monitored at the specified time interval
+  every_n_epochs: 100 # number of epochs between checkpoints
+  save_on_train_epoch_end: null # whether to run checkpointing at the end of the training epoch or the end of validation

configs/callbacks/model_summary.yaml

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+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html
+
+model_summary:
+  _target_: lightning.pytorch.callbacks.RichModelSummary
+  max_depth: 3 # the maximum depth of layer nesting that the summary will include

configs/callbacks/rich_progress_bar.yaml

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+# https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html
+
+rich_progress_bar:
+  _target_: lightning.pytorch.callbacks.RichProgressBar