noether.core.callbacks.checkpoint.best_checkpoint¶

Classes¶

BestCheckpointCallback

Callback to save the best model based on a metric.

Module Contents¶

class noether.core.callbacks.checkpoint.best_checkpoint.BestCheckpointCallback(callback_config, **kwargs)¶

Bases: noether.core.callbacks.periodic.PeriodicCallback

Callback to save the best model based on a metric.

This callback monitors a specified metric and saves the model checkpoint whenever a new best value is achieved. It supports storing different model components when using a composite model and can save checkpoints at different tolerance thresholds.

Example config:

callbacks:
  - kind: noether.core.callbacks.BestCheckpointCallback
    name: BestCheckpointCallback
    every_n_epochs: 1
    metric_key: loss/val/total
    model_names:  # only applies when training a CompositeModel
      - encoder

Parameters:

callback_config (noether.core.schemas.callbacks.BestCheckpointCallbackConfig) – Configuration for the callback. See BestCheckpointCallbackConfig for available options including metric key, model names, and tolerance settings.
**kwargs – Additional arguments passed to the parent class.

metric_key¶

model_names¶

higher_is_better¶

best_metric_value¶

save_frozen_weights¶

tolerances_is_exceeded¶

tolerance_counter = 0¶

metric_at_exceeded_tolerance: dict[float, float]¶

state_dict()¶

Return the state of the callback for checkpointing.

Returns:: Dictionary containing the best metric value, tolerance tracking state, and counter information.
Return type:: dict[str, Any]

load_state_dict(state_dict)¶

Load the callback state from a checkpoint.

Note

This modifies the input state_dict in place.

Parameters:: state_dict (dict[str, Any]) – Dictionary containing the saved callback state.
Return type:: None

before_training(*, update_counter)¶

Validate callback configuration before training starts.

Parameters:: update_counter – The training update counter.
Raises:: NotImplementedError – If resuming training with tolerances is attempted.
Return type:: None

periodic_callback(**_)¶

Execute the periodic callback to check and save best model.

This method is called at the configured frequency (e.g., every N epochs). It checks if the current metric value is better than the previous best, and if so, saves the model checkpoint. Also tracks tolerance-based checkpoints.

Raises:: KeyError – If the log cache is empty or the metric key is not found.
Return type:: None

after_training(**kwargs)¶

Log the best metric values at different tolerance thresholds after training completes.

Parameters:: **kwargs – Additional keyword arguments (unused).
Return type:: None