Source code for optuna.integration.pytorch_lightning

import optuna

if optuna.type_checking.TYPE_CHECKING:
    from typing import Dict  # NOQA
    from typing import Optional  # NOQA

try:
    from pytorch_lightning.callbacks import EarlyStopping
    from pytorch_lightning import LightningModule
    from pytorch_lightning import Trainer

    _available = True
except (ImportError, SyntaxError) as e:
    # SyntaxError is raised with Python versions below 3.6 since PyTorch Lightning does not
    # support them.
    _import_error = e
    # PyTorchLightningPruningCallback is disabled because PyTorch Lightning is not available.
    _available = False
    EarlyStopping = object


[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_end`` and the names thus depend on how this dictionary is formatted. """ def __init__(self, trial, monitor): # type: (optuna.trial.Trial, str) -> None super(PyTorchLightningPruningCallback, self).__init__(monitor=monitor) _check_pytorch_lightning_availability() self._trial = trial self._monitor = monitor def on_epoch_end(self, trainer, pl_module): # type: (Trainer, 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.exceptions.TrialPruned(message)
def _check_pytorch_lightning_availability(): # type: () -> None if not _available: raise ImportError( "PyTorch Lightning is not available. Please install PyTorch Lightning to use this " "feature. PyTorch Lightning can be installed by executing `$ pip install " "pytorch-lightning`. For further information, please refer to the installation guide " "of PyTorch Lightning. (The actual import error is as follows: " + str(_import_error) + ")" )