The Strategy Registry is experimental and subject to change.
Lightning includes a registry that holds information about Training strategies and allows for the registration of new custom strategies.
The Strategies 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 Strategy that were defined during registration.
# Training with the DDP Strategy 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 Strategy with `debug` as True trainer = Trainer(strategy="tpu_spawn_debug", accelerator="tpu", devices=8)
Additionally, you can pass your custom registered training strategies to the
from pytorch_lightning.strategies import DDPStrategy, StrategyRegistry, 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 Strategy with your custom CheckpointIO plugin StrategyRegistry.register( "ddp_custom_checkpoint_io", DDPStrategy, description="DDP Strategy with custom checkpoint io plugin", checkpoint_io=custom_checkpoint_io, ) trainer = Trainer(strategy="ddp_custom_checkpoint_io", accelerator="gpu", devices=2)