huggingface-accelerate

You can run distributed PyTorch training across multiple GPUs using HuggingFace Accelerate to automate device placement and provide a unified API for DDP, DeepSpeed, FSDP, and Megatron. This reduces boilerplate by managing the backend setup automatically.

Scaling HuggingFace Transformers models to multi-node clusters is best achieved using a unified API that manages automatic device placement and mixed precision. This approach enables seamless scaling from a single GPU to multi-node training with a single command.

Yes, you can use mixed precision training with FP16, BF16, and FP8 formats. Accelerate provides automatic mixed precision management across Linux, macOS, and Windows, handling device placement and backend configurations for your PyTorch models.

HuggingFace Accelerate supports a unified API across DeepSpeed, FSDP, DDP, and Megatron backends. This allows you to configure and run distributed PyTorch training across these different backends without changing your core training script.

You configure your environment for multi-GPU training interactively using the accelerate config command. After generating the configuration, you prepare your model with the Accelerator and launch the distributed training workflow using a single command.