CheckpointIO¶

class pytorch_lightning.plugins.io.CheckpointIO[source]¶

Bases: abc.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.

abstract load_checkpoint(path, storage_options=None)[source]¶

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

Parameters

path¶ (Union[str, Path]) – Path to checkpoint
storage_options¶ (Optional[Any]) – Optional parameters when loading the model/training states.

Returns: The loaded checkpoint.

Return type: Dict[str, Any]

abstract remove_checkpoint(path)[source]¶

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)[source]¶

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