CheckpointIO

Bases: 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()]).

abstract load_checkpoint(path, map_location=None, weights_only=None)

Load checkpoint from a path when resuming or loading ckpt for test/validate/predict stages.

Parameters:

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

• map_location (Optional[Any]) – 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.

abstract remove_checkpoint(path)

Remove checkpoint file from the filesystem.

Parameters:

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

Return type:

None

abstract 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]) – Optional parameters when saving the model/training states.

Return type:

None

teardown()

This method is called to teardown the process.

Return type:

None