Callback — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

class lightning.pytorch.callbacks.Callback[source]

Bases: object

Abstract base class used to build new callbacks.

Subclass this class and override any of the relevant hooks

load_state_dict(state_dict)[source]

Called when loading a checkpoint, implement to reload callback state given callback’s state_dict.

Parameters:

state_dict (dict[str, Any]) – the callback state returned by state_dict.

Return type:

None

on_after_backward(trainer, pl_module)[source]

Called after loss.backward() and before optimizers are stepped.

Return type:

None

on_before_backward(trainer, pl_module, loss)[source]

Called before loss.backward().

Return type:

None

on_before_optimizer_step(trainer, pl_module, optimizer)[source]

Called before optimizer.step().

Return type:

None

on_before_zero_grad(trainer, pl_module, optimizer)[source]

Called before optimizer.zero_grad().

Return type:

None

on_exception(trainer, pl_module, exception)[source]

Called when any trainer execution is interrupted by an exception.

Return type:

None

on_fit_end(trainer, pl_module)[source]

Called when fit ends.

Return type:

None

on_fit_start(trainer, pl_module)[source]

Called when fit begins.

Return type:

None

on_load_checkpoint(trainer, pl_module, checkpoint)[source]

Called when loading a model checkpoint, use to reload state.

Parameters:

Return type:

None

on_predict_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)[source]

Called when the predict batch ends.

Return type:

None

on_predict_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)[source]

Called when the predict batch begins.

Return type:

None

on_predict_end(trainer, pl_module)[source]

Called when predict ends.

Return type:

None

on_predict_epoch_end(trainer, pl_module)[source]

Called when the predict epoch ends.

Return type:

None

on_predict_epoch_start(trainer, pl_module)[source]

Called when the predict epoch begins.

Return type:

None

on_predict_start(trainer, pl_module)[source]

Called when the predict begins.

Return type:

None

on_sanity_check_end(trainer, pl_module)[source]

Called when the validation sanity check ends.

Return type:

None

on_sanity_check_start(trainer, pl_module)[source]

Called when the validation sanity check starts.

Return type:

None

on_save_checkpoint(trainer, pl_module, checkpoint)[source]

Called when saving a checkpoint to give you a chance to store anything else you might want to save.

Parameters:

Return type:

None

on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)[source]

Called when the test batch ends.

Return type:

None

on_test_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)[source]

Called when the test batch begins.

Return type:

None

on_test_end(trainer, pl_module)[source]

Called when the test ends.

Return type:

None

on_test_epoch_end(trainer, pl_module)[source]

Called when the test epoch ends.

Return type:

None

on_test_epoch_start(trainer, pl_module)[source]

Called when the test epoch begins.

Return type:

None

on_test_start(trainer, pl_module)[source]

Called when the test begins.

Return type:

None

on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)[source]

Called when the train batch ends. :rtype: None

Note

The value outputs["loss"] here will be the normalized value w.r.t accumulate_grad_batches of the loss returned from training_step.

on_train_batch_start(trainer, pl_module, batch, batch_idx)[source]

Called when the train batch begins.

Return type:

None

on_train_end(trainer, pl_module)[source]

Called when the train ends.

Return type:

None

on_train_epoch_end(trainer, pl_module)[source]

Called when the train epoch ends.

To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of thelightning.pytorch.core.LightningModule and access them in this hook:

class MyLightningModule(L.LightningModule): def init(self): super().init() self.training_step_outputs = []

def training_step(self):
    loss = ...
    self.training_step_outputs.append(loss)
    return loss

class MyCallback(L.Callback): def on_train_epoch_end(self, trainer, pl_module): # do something with all training_step outputs, for example: epoch_mean = torch.stack(pl_module.training_step_outputs).mean() pl_module.log("training_epoch_mean", epoch_mean) # free up the memory pl_module.training_step_outputs.clear()

Return type:

None

on_train_epoch_start(trainer, pl_module)[source]

Called when the train epoch begins.

Return type:

None

on_train_start(trainer, pl_module)[source]

Called when the train begins.

Return type:

None

on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)[source]

Called when the validation batch ends.

Return type:

None

on_validation_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)[source]

Called when the validation batch begins.

Return type:

None

on_validation_end(trainer, pl_module)[source]

Called when the validation loop ends.

Return type:

None

on_validation_epoch_end(trainer, pl_module)[source]

Called when the val epoch ends.

Return type:

None

on_validation_epoch_start(trainer, pl_module)[source]

Called when the val epoch begins.

Return type:

None

on_validation_start(trainer, pl_module)[source]

Called when the validation loop begins.

Return type:

None

setup(trainer, pl_module, stage)[source]

Called when fit, validate, test, predict, or tune begins.

Return type:

None

state_dict()[source]

Called when saving a checkpoint, implement to generate callback’s state_dict.

Return type:

dict[str, Any]

Returns:

A dictionary containing callback state.

teardown(trainer, pl_module, stage)[source]

Called when fit, validate, test, predict, or tune ends.

Return type:

None

property state_key_: str_

Identifier for the state of the callback.

Used to store and retrieve a callback’s state from the checkpoint dictionary bycheckpoint["callbacks"][state_key]. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.