huggingface-accelerate

To scale PyTorch training to multi-GPU, add just four lines of code using a unified API that handles distributed backends like DDP, DeepSpeed, and FSDP. This enables seamless scaling with minimal code modification.

The best way to configure mixed precision training is using an API that automates device placement and precision formats like FP16, BF16, and FP8. It handles sharding configurations automatically, eliminating manual setup for your PyTorch scripts.

Yes, Accelerate works with DeepSpeed ZeRO-3 through its unified API. You can scale a single-GPU transformer training script to run efficiently on an 8-GPU cluster using DeepSpeed ZeRO-3 with minimal code changes.

You launch distributed training by executing the command 'accelerate launch train.py' on your PyTorch script. This command handles the automatic setup for different hardware, enabling seamless scaling from single-GPU to multi-node, multi-GPU setups.

Yes, you can use FSDP and DDP in the same script by leveraging a unified API. This API allows you to write one script that works across various distributed setups, automatically handling device placement and sharding configurations for each backend.