Source code for pytorch_lightning.plugins.io.checkpoint_plugin
# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from typing import Any, Dict, Optional from pytorch_lightning.utilities.types import _PATH[docs]class CheckpointIO(ABC): """Interface to save/load checkpoints as they are saved through the ``TrainingTypePlugin``. Typically most plugins either use the Torch based IO Plugin; ``TorchCheckpointIO`` but may require particular handling depending on the plugin. In addition, you can pass a custom ``CheckpointIO`` by extending this class and passing it to the Trainer, i.e ``Trainer(plugins=[MyCustomCheckpointIO()])``. .. note:: For some plugins, it is not possible to use a custom checkpoint plugin as checkpointing logic is not modifiable. """[docs] @abstractmethod def save_checkpoint(self, checkpoint: Dict[str, Any], path: _PATH, storage_options: Optional[Any] = None) -> None: """Save model/training states as a checkpoint file through state-dump and file-write. Args: checkpoint: dict containing model and trainer state path: write-target path storage_options: Optional parameters when saving the model/training states. """[docs] @abstractmethod def load_checkpoint(self, path: _PATH, storage_options: Optional[Any] = None) -> Dict[str, Any]: """Load checkpoint from a path when resuming or loading ckpt for test/validate/predict stages. Args: path: Path to checkpoint storage_options: Optional parameters when loading the model/training states. Returns: The loaded checkpoint. """[docs] @abstractmethod def remove_checkpoint(self, path: _PATH) -> None: """Remove checkpoint file from the filesystem. Args: path: Path to checkpoint """