class optuna.integration.PyTorchLightningPruningCallback(trial: optuna.trial._trial.Trial, monitor: str)[source]

PyTorch Lightning callback to prune unpromising trials.

See the example if you want to add a pruning callback which observes accuracy.

  • trial – A Trial corresponding to the current evaluation of the objective function.

  • monitor – An evaluation metric for pruning, e.g., val_loss or val_acc. The metrics are obtained from the returned dictionaries from e.g. pytorch_lightning.LightningModule.training_step or pytorch_lightning.LightningModule.validation_end and the names thus depend on how this dictionary is formatted.

__init__(trial: optuna.trial._trial.Trial, monitor: str)None[source]

Initialize self. See help(type(self)) for accurate signature.


__init__(trial, monitor)

Initialize self.

on_epoch_end(trainer, pl_module)

on_validation_end(trainer, pl_module)