espnet3.components.callbacks.default_callbacks.get_default_callbacks

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espnet3.components.callbacks.default_callbacks.get_default_callbacks(exp_dir: str = './exp', log_interval: int = 500, best_model_criterion: List[Tuple[str, int, str]] | List[List] = [('valid/loss', 3, 'min')]) → List[Callback]

Return a list of callbacks tailored for most training workflows.

Includes.

• ModelCheckpoint for saving the last model checkpoint (save_last)
• One or more ModelCheckpoint instances for saving the top-K checkpoints according to : specific metrics
• AverageCheckpointsCallback to compute and save the average model from top-K : checkpoints
• LearningRateMonitor to track and log learning rates during training
• MetricsLogger to emit train and validation summaries
• TQDMProgressBar to show a rich progress bar during training

• Parameters:
  • exp_dir (str) – Directory to store checkpoints and logs.
  • log_interval (int) – Frequency (in training steps) to refresh the progress bar.
  • best_model_criterion (List *[*Tuple *[*str , int , str ] ]) – Criteria for saving top-K checkpoints. Each tuple is (name, top_k, mode), where name is the validation value to monitor, top_k is the number of retained checkpoints, and mode is "min" or "max".
• Returns: A list of callbacks to be passed to the PyTorch Lightning : Trainer.
• Return type: List[Callback]

Example

>>> from default_callbacks import get_default_callbacks
>>> callbacks = get_default_callbacks(
...     exp_dir="./exp",
...     log_interval=100,
...     best_model_criterion=[("val/loss", 5, "min"), ("val/acc", 3, "max")]
... )
>>> trainer = Trainer(callbacks=callbacks, ...)