huggingface-accelerate

To simplify distributed training for PyTorch, you can use a unified API that automates device placement and mixed precision. By initializing an accelerator object, your script gains automatic support for running large-scale models across multiple GPUs, TPUs, or nodes.

PyTorch distributed training supports DeepSpeed and FSDP. The unified API natively integrates with DeepSpeed, FSDP, DDP, and Megatron to manage large-scale training across multiple GPUs, TPUs, or nodes while handling automatic device placement.

To add multi-GPU support to an existing PyTorch script, use a unified API that simplifies the process into a few lines. You initialize an accelerator object in your script, which automatically manages device placement and mixed precision for your training loop.

You do not need to manually configure mixed precision for PyTorch distributed training. The unified API handles mixed precision and automatic device placement automatically, streamlining the setup process for running large-scale models across multiple GPUs.

Automating distributed training setup for large-scale models requires the Accelerate, PyTorch, and Transformers libraries. You also need the respective distributed training libraries like DeepSpeed, FSDP, DDP, or Megatron depending on your specific hardware configuration.

You can use a single unified API for PyTorch distributed training across TPUs and nodes. This API simplifies adding distributed support to any PyTorch script, allowing interactive configuration of hardware options for large-scale model training.