TorchCheckpointIO

class lightning.fabric.plugins.io.torch_io.TorchCheckpointIO

Bases: CheckpointIO

CheckpointIO that utilizes torch.save() and torch.load() to save and load checkpoints respectively, common for most use cases.

Warning

This is an experimental feature.

load_checkpoint(path, map_location=<function TorchCheckpointIO.<lambda>>, weights_only=None)

Loads checkpoint using torch.load(), with additional handling for fsspec remote loading of files.

Parameters:

• path (Union[str, Path]) – Path to checkpoint

• map_location (Optional[Callable]) – a function, torch.device, string or a dict specifying how to remap storage locations.

• weights_only (Optional[bool]) – Defaults to None. If True, restricts loading to state_dicts of plain torch.Tensor and other primitive types. If loading a checkpoint from a trusted source that contains an nn.Module, use weights_only=False. If loading checkpoint from an untrusted source, we recommend using weights_only=True. For more information, please refer to the PyTorch Developer Notes on Serialization Semantics.

Return type:

dict[str, Any]

Returns: The loaded checkpoint.

Raises:

FileNotFoundError – If path is not found by the fsspec filesystem

remove_checkpoint(path)

Remove checkpoint file from the filesystem.

Parameters:

path (Union[str, Path]) – Path to checkpoint

Return type:

None

save_checkpoint(checkpoint, path, storage_options=None)

Save model/training states as a checkpoint file through state-dump and file-write.

Parameters:

• checkpoint (dict[str, Any]) – dict containing model and trainer state

• path (Union[str, Path]) – write-target path

• storage_options (Optional[Any]) – not used in TorchCheckpointIO.save_checkpoint

Raises:

TypeError – If storage_options arg is passed in

Return type:

None