optuna.integration.PyTorchLightningPruningCallback¶
- 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.
- Parameters
trial – A
Trial
corresponding to the current evaluation of the objective function.monitor – An evaluation metric for pruning, e.g.,
val_loss
orval_acc
. The metrics are obtained from the returned dictionaries from e.g.pytorch_lightning.LightningModule.training_step
orpytorch_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]¶
Methods
__init__
(trial, monitor)on_epoch_end
(trainer, pl_module)on_validation_end
(trainer, pl_module)