huggingface--transformers/docs/source/en/accelerate.md

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# Accelerate

[Accelerate](https://hf.co/docs/accelerate/index) provides a unified interface for distributed training backends like [FSDP](https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html) or [DeepSpeed](https://www.deepspeed.ai/). It detects your environment (number of GPUs, distributed backend, mixed precision, etc.) and automatically configures training, whether you're on 1 GPU with DDP or 8 GPUs with FSDP.

Accelerate wraps the model in the appropriate distributed wrapper, moves it to the correct device, and creates a compatible optimizer. During training, Accelerate uses its own [`~accelerate.Accelerator.backward`] method to handle gradient scaling for mixed precision. [`Trainer`] calls the appropriate Accelerate APIs and delegates all distributed mechanics to Accelerate.

Configure Accelerate for [`Trainer`] with either an Accelerate config file or [`TrainingArguments`].

## Accelerate config file

Run the [accelerate config](https://huggingface.co/docs/accelerate/en/package_reference/cli#accelerate-config) command and answer questions about your hardware and training setup. This creates a `default_config.yaml` file in your cache. The example below is for FSDP.

```yaml
compute_environment: LOCAL_MACHINE
distributed_type: FSDP
fsdp_config:
  fsdp_version: 2
  fsdp_reshard_after_forward: true
  fsdp_cpu_offload: false
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_cpu_ram_efficient_loading: true
  fsdp_activation_checkpointing: false
  fsdp_state_dict_type: SHARDED_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
mixed_precision: bf16
num_machines: 1
num_processes: 4
```

Run [accelerate launch](https://huggingface.co/docs/accelerate/en/package_reference/cli#accelerate-launch) with a [`Trainer`]-based script, and Accelerate reads the config file to set up training. The [`~TrainingArguments#fsdp_config`] and [`~TrainingArguments#deepspeed`] args are unnecessary because the Accelerate config file covers the same settings.

```cli
accelerate launch train.py
```

The [`~TrainingArguments#accelerator_config`] accepts settings that don't have dedicated top-level arguments. For example, set `non_blocking=True` together with [`~TrainingArguments.dataloader_pin_memory`] to overlap data transfer with compute for higher GPU throughput.

```py
from transformers import TrainingArguments

TrainingArguments(
    ...,
    dataloader_pin_memory=True,
    accelerator_config={
        "non_blocking": True,
    },
)
```

## TrainingArguments

Pass a backend-specific config to [`TrainingArguments`]. The [`~Trainer.create_accelerator_and_postprocess`] method reads the settings and configures training.

<hfoptions id="backend">
<hfoption id="FSDP">

Pass a JSON config file or dict to [`~TrainingArguments.fsdp_config`]. See [FSDP](./fsdp) for a full guide and config reference.

```py
from transformers import TrainingArguments

TrainingArguments(
    ...,
    fsdp=True,
    fsdp_config="path/to/fsdp.json",
)
```

</hfoption>
<hfoption id="DeepSpeed">

Pass a JSON config file or dict to [`~TrainingArguments.deepspeed`]. See [DeepSpeed](./deepspeed) for a full guide and config reference.

```py
from transformers import TrainingArguments

TrainingArguments(
    ...,
    deepspeed="path/to/ds_config.json",
)
```

</hfoption>
<hfoption id="DDP">

DDP is configured directly through [`TrainingArguments`] fields. See [DDP](./ddp) for details.

```py
from transformers import TrainingArguments

TrainingArguments(
    ...,
    ddp_backend="nccl",
    ddp_find_unused_parameters=False,
    ddp_bucket_cap_mb=25,
    ddp_timeout=1800,
)
```

</hfoption>
</hfoptions>

## Next steps

- See [DDP](./ddp) for data-parallel training when your model fits on one GPU.
- See [FSDP](./fsdp) for sharding parameters, gradients, and optimizer states across GPUs.
- See [DeepSpeed](./deepspeed) for ZeRO optimization and offloading.