noether.core.callbacks.checkpoint.checkpoint¶

Classes¶

CheckpointCallback

Callback to save the model and optimizer state periodically.

Module Contents¶

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

Bases: noether.core.callbacks.periodic.PeriodicCallback

Callback to save the model and optimizer state periodically.

Example config:

- kind: noether.core.callbacks.CheckpointCallback
  name: CheckpointCallback
  every_n_epochs: 1
  save_weights: true
  save_optim: true

Parameters:

callback_config (noether.core.schemas.callbacks.CheckpointCallbackConfig) – Configuration for the callback. See CheckpointCallbackConfig for available options.
**kwargs – Additional arguments passed to the parent class.

save_weights¶

save_optim¶

save_latest_weights¶

save_latest_optim¶

model_names¶

before_training(*, update_counter)¶

Hook called once before the training loop starts.

This method is intended to be overridden by derived classes to perform initialization tasks before training begins. Common use cases include:

Initializing experiment tracking (e.g., logging hyperparameters)
Printing model summaries or architecture details
Initializing specific data structures or buffers needed during training
Performing sanity checks on the data or configuration

Note

This method is executed within a torch.no_grad() context.

Parameters:: update_counter (noether.core.utils.training.UpdateCounter) – UpdateCounter instance to access current training progress.
Return type:: None

periodic_callback(*, interval_type, update_counter, **kwargs)¶

Hook called periodically based on the configured intervals.

This method is the primary entry point for periodic actions in subclasses. It is triggered when any of the configured intervals (every_n_epochs, every_n_updates, or every_n_samples) are reached.

Subclasses should override this method to implement periodic logic such as:

Calculating and logging expensive validation metrics
Saving specific model checkpoints or artifacts
Visualizing training progress (e.g., plotting samples)
Adjusting training hyperparameters or model state

Note

This method is executed within a torch.no_grad() context.

Parameters:

interval_type (noether.core.callbacks.periodic.IntervalType) – “epoch”, “update”, “sample” or “eval” indicating which interval triggered this callback.
update_counter (noether.core.utils.training.UpdateCounter) – UpdateCounter instance providing details about the current training progress (epoch, update, sample counts).
**kwargs – Additional keyword arguments passed from the triggering hook (e.g., from after_epoch() or after_update()).

Return type:

None

after_training(**_)¶

Hook called once after the training loop finishes.

This method is intended to be overridden by derived classes to perform cleanup or final reporting tasks after training is complete. Common use cases include:

Performing a final evaluation on the test set
Saving final model weights or artifacts
Sending notifications (e.g., via Slack or email) about the completed run
Closing or finalizing experiment tracking sessions

Note

This method is executed within a torch.no_grad() context.

Parameters:: update_counter – UpdateCounter instance to access current training progress.
Return type:: None