huggingface-accelerate

Distributed training across multiple GPUs requires minimal code changes when using a unified API that automates device placement and launch orchestration. You can scale from single-node to multi-node training scenarios with just a few lines of code.

A single script can handle FSDP and DeepSpeed training through a unified API. This approach abstracts the backend differences, allowing you to switch between distributed training strategies without rewriting your core logic.

Mixed precision training with FP16 and BF16 is fully supported. The library automatically handles precision settings during the device placement process, enabling faster computation and reduced memory usage.

The best way to launch a multi-GPU training job is using an interactive config and single-launch workflow. This config-driven approach ensures seamless compatibility with the HuggingFace ecosystem.

Manual device placement is not required when scaling from CPU to multi-GPU environments. The unified API automatically handles device mapping and launch orchestration for your models.

A unified API is ideal when you need to quickly scale experiments across different hardware setups without boilerplate changes. It abstracts configuration complexities, whereas native DDP requires explicit setup for each environment.