optuna.integration.PyTorchLightningPruningCallback

class optuna.integration.PyTorchLightningPruningCallback(trial, monitor)[source]

PyTorch Lightning callback to prune unpromising trials.

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

Parameters

trial (Trial) – A Trial corresponding to the current evaluation of the objective function.
monitor (str) – 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_epoch_end and the names thus depend on how this dictionary is formatted.

Methods

on_validation_end(trainer, pl_module)