---
title: Strategy registry
description: >-
  Lightning includes a registry that holds information about Training strategies
  and allows for the registration of new custom strategies.
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/advanced/strategy_registry'
---
# Strategy registry {#strategy-registry}

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.

```python
# Training with the DDP Strategy
trainer = Trainer(strategy="ddp", 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="xla_debug", accelerator="tpu", devices=8)
```

Additionally, you can pass your custom registered training strategies to the `strategy` argument.

```python
from lightning.pytorch.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)
```

---

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- Parent: [Advanced](/docs/pytorch/2.6.6/reference/advanced.md)