Source code for optuna.integration.pytorch_lightning

import optuna


with optuna._imports.try_import() as _imports:
    from pytorch_lightning import LightningModule
    from pytorch_lightning import Trainer
    from pytorch_lightning.callbacks import EarlyStopping

if not _imports.is_successful():
    EarlyStopping = object  # NOQA
    LightningModule = object  # NOQA
    Trainer = object  # NOQA


[docs]class PyTorchLightningPruningCallback(EarlyStopping): """PyTorch Lightning callback to prune unpromising trials. See `the example <https://github.com/optuna/optuna/blob/master/ examples/pytorch_lightning_simple.py>`__ if you want to add a pruning callback which observes accuracy. Args: trial: A :class:`~optuna.trial.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_epoch_end`` and the names thus depend on how this dictionary is formatted. """ def __init__(self, trial: optuna.trial.Trial, monitor: str) -> None: _imports.check() super(PyTorchLightningPruningCallback, self).__init__(monitor=monitor) self._trial = trial def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None: logs = trainer.callback_metrics epoch = pl_module.current_epoch current_score = logs.get(self.monitor) if current_score is None: return self._trial.report(current_score, step=epoch) if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.TrialPruned(message)