huggingface-accelerate

To scale PyTorch training across multiple GPUs with minimal changes, use the HuggingFace Accelerate library to automatically handle device placement and distributed setup. It allows rapid scaling of workloads to multi-GPU and multi-node environments using a unified API.

Yes, HuggingFace Accelerate supports both DeepSpeed and FSDP for distributed training. It provides a unified API that also includes DDP and Megatron, allowing you to configure sharding and mixed precision automatically across these backends.

Yes, you can use mixed precision including FP16, BF16, and FP8 when running distributed PyTorch workloads. Accelerate provides automatic configuration for mixed precision alongside device placement and sharding during training.

The best way to configure multi-node distributed training for large language models is using a unified API like Accelerate. It reduces boilerplate code for distributed setups, enabling rapid iteration and efficient scaling across multiple machines with simple commands.

No, you do not need to manually handle device placement and sharding when using FSDP. HuggingFace Accelerate provides automatic configuration for these aspects, drastically simplifying the process of adding distributed training capabilities to your scripts.