Training Type Plugins Registry

Warning

The Plugins Registry is experimental and subject to change.

Lightning includes a registry that holds information about Training Type plugins and allows for the registration of new custom plugins.

The Plugins are assigned strings that identify them, such as “ddp”, “deepspeed_stage_2_offload”, and so on. It also returns the optional description and parameters for initialising the Plugin that were defined during registration.

# Training with the DDP Plugin with `find_unused_parameters` as False
trainer = Trainer(strategy="ddp_find_unused_parameters_false", accelerator="gpu", devices=4)

# Training with DeepSpeed ZeRO Stage 3 and CPU Offload
trainer = Trainer(strategy="deepspeed_stage_3_offload", accelerator="gpu", devices=3)

# Training with the TPU Spawn Plugin with `debug` as True
trainer = Trainer(strategy="tpu_spawn_debug", accelerator="tpu", devices=8)

Additionally, you can pass your custom registered training type plugins to the strategy argument.

from pytorch_lightning.plugins import DDPPlugin, TrainingTypePluginsRegistry, CheckpointIO


class CustomCheckpointIO(CheckpointIO):
    def save_checkpoint(self, checkpoint: Dict[str, Any], path: Union[str, Path]) -> None:
        ...

    def load_checkpoint(self, path: Union[str, Path]) -> Dict[str, Any]:
        ...


custom_checkpoint_io = CustomCheckpointIO()

# Register the DDP Plugin with your custom CheckpointIO plugin
TrainingTypePluginsRegistry.register(
    "ddp_custom_checkpoint_io",
    DDPPlugin,
    description="DDP Plugin with custom checkpoint io plugin",
    checkpoint_io=custom_checkpoint_io,
)

trainer = Trainer(strategy="ddp_custom_checkpoint_io", accelerator="gpu", devices=2)