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Training Type Plugins Registry


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
    description="DDP Plugin with custom checkpoint io plugin",

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