MultiTrainingCallback

class MultiTrainingCallback(callbacks: str | TrainingCallback | type[TrainingCallback] | None | Sequence[str | TrainingCallback | type[TrainingCallback] | None] = None, callbacks_kwargs: Mapping[str, Any] | None | Sequence[Mapping[str, Any] | None] = None)

Bases: TrainingCallback

A wrapper for calling multiple training callbacks together.

Initialize the callback.

Note

the constructor allows “broadcasting” of callbacks, i.e., proving a single callback, but a list of callback kwargs. In this case, for each element of this list the given callback is instantiated.

Parameters:

• callbacks (list[TrainingCallback]) – the callbacks

• callbacks_kwargs (TrainingCallbackKwargsHint) – additional keyword-based parameters for instantiating the callbacks

Methods Summary

on_batch(epoch, batch, batch_loss, **kwargs)	Call for training batches.
post_batch(epoch, batch, **kwargs)	Call for training batches.
post_epoch(epoch, epoch_loss, **kwargs)	Call after epoch.
post_train(losses, **kwargs)	Call after training.
pre_batch(**kwargs)	Call before training batch.
pre_step(**kwargs)	Call before the optimizer's step.
register_callback(callback)	Register a callback.
register_training_loop(training_loop)	Register the training loop.

Methods Documentation

on_batch(epoch: int, batch, batch_loss: float, **kwargs: Any) → None

Call for training batches.

Parameters:

• epoch (int)

• batch_loss (float)

• kwargs (Any)

Return type:

None

post_batch(epoch: int, batch, **kwargs: Any) → None

Call for training batches.

Parameters:

• epoch (int)

• kwargs (Any)

Return type:

None

post_epoch(epoch: int, epoch_loss: float, **kwargs: Any) → None

Call after epoch.

Parameters:

• epoch (int)

• epoch_loss (float)

• kwargs (Any)

Return type:

None

post_train(losses: list[float], **kwargs: Any) → None

Call after training.

Parameters:

• losses (list[float])

• kwargs (Any)

Return type:

None

pre_batch(**kwargs: Any) → None

Call before training batch.

Parameters:

kwargs (Any)

Return type:

None

pre_step(**kwargs: Any) → None

Call before the optimizer’s step.

Parameters:

kwargs (Any)

Return type:

None

register_callback(callback: TrainingCallback) → None

Register a callback.

Parameters:

callback (TrainingCallback)

Return type:

None

register_training_loop(training_loop: TrainingLoop) → None

Register the training loop.

Parameters:

training_loop (TrainingLoop)

Return type:

None