huggingface-accelerate

Distributed PyTorch training across multiple GPUs can be run with a single script by using Accelerate to provide a unified API that handles device placement, mixed precision, and parallelism strategies. It enables a 4-line code setup to turn any PyTorch script into a distributed run.

DeepSpeed and FSDP configurations can be used in the same PyTorch training workflow through a unified API that streamlines deployment across different parallelism strategies. This allows seamless switching between distributed strategies without requiring extensive codebase modifications.

The best way to configure mixed precision and device placement for multi-GPU setups is through an interactive configuration generation process. This automatically handles device placement and mixed precision settings to streamline quick launches across CPU, single-GPU, and multi-GPU environments.

Megatron-LM integration works with standard Accelerate configurations by offering optional support alongside DDP, DeepSpeed, and FSDP. This allows users to incorporate tensor, pipeline, and data parallelism configurations into their distributed PyTorch training scripts.

You do not need to heavily modify your PyTorch script for different parallelism strategies because Accelerate streamlines distributed training by enabling a single script workflow. It abstracts away the complexity of switching between CPU, single-GPU, and multi-GPU setups with just four lines of code.