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# 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.
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# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.
from abc import ABC, abstractmethod
from typing import Any, Dict, Optional

from lightning_fabric.utilities.types import _PATH

[docs]class CheckpointIO(ABC): """Interface to save/load checkpoints as they are saved through the ``Strategy``. 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, map_location: 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 map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage locations. Returns: The loaded checkpoint. """
[docs] @abstractmethod def remove_checkpoint(self, path: _PATH) -> None: """Remove checkpoint file from the filesystem. Args: path: Path to checkpoint """
[docs] def teardown(self) -> None: """This method is called to teardown the process."""

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