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 – 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_epoch_end
and the names thus depend on how this dictionary is formatted.
Methods
on_validation_end
(trainer, pl_module)