Source code for lightning.fabric.plugins.io.checkpoint_io
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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``.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
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
"""