LambdaCallback¶

class lightning.pytorch.callbacks.LambdaCallback(setup=None, teardown=None, on_fit_start=None, on_fit_end=None, on_sanity_check_start=None, on_sanity_check_end=None, on_train_batch_start=None, on_train_batch_end=None, on_train_epoch_start=None, on_train_epoch_end=None, on_validation_epoch_start=None, on_validation_epoch_end=None, on_test_epoch_start=None, on_test_epoch_end=None, on_validation_batch_start=None, on_validation_batch_end=None, on_test_batch_start=None, on_test_batch_end=None, on_train_start=None, on_train_end=None, on_validation_start=None, on_validation_end=None, on_test_start=None, on_test_end=None, on_exception=None, on_save_checkpoint=None, on_load_checkpoint=None, on_before_backward=None, on_after_backward=None, on_before_optimizer_step=None, on_before_zero_grad=None, on_predict_start=None, on_predict_end=None, on_predict_batch_start=None, on_predict_batch_end=None, on_predict_epoch_start=None, on_predict_epoch_end=None)[source]¶

Bases: Callback

Create a simple callback on the fly using lambda functions.

Parameters:: **kwargs¶ – hooks supported by Callback

Example:

>>> from lightning.pytorch import Trainer
>>> from lightning.pytorch.callbacks import LambdaCallback
>>> trainer = Trainer(callbacks=[LambdaCallback(setup=lambda *args: print('setup'))])