huggingface-accelerate

You can scale PyTorch distributed training across multiple GPUs with minimal code changes by using a unified API that automates device placement, mixed precision, and multi-GPU configuration with just a few lines of code and a single launch command.

The best way to run a single-GPU PyTorch script on a multi-GPU cluster is to adapt it using a unified API that automatically handles distributed setup, enabling rapid scaling to 8 GPUs or more with minimal modifications.

Yes, this distributed training approach supports DeepSpeed and FSDP backends, integrating seamlessly with them along with Megatron and DDP through a single unified API to simplify multi-node and multi-GPU setups.

Mixed precision training for deep learning models on multiple GPUs works by automatically handling FP16, BF16, and FP8 configurations, streamlining the distributed setup process so you can focus on model logic rather than manual device placement.

You do not need to rewrite your PyTorch script to use FSDP for multi-node training; you can adapt your existing script to run on a cluster of 8 GPUs with just a few lines of code and a single launch command.