huggingface-accelerate

Distributed training in PyTorch can be simplified using Accelerate's unified API to automate device placement and launcher configuration. This reduces manual boilerplate for hardware setups, enabling smooth scalability across single- and multi-GPU scenarios.

Yes, a single PyTorch training script can run across DDP, DeepSpeed, FSDP, and Megatron-LM using Accelerate. The unified API allows your script to operate across different hardware configurations and distributed strategies without requiring separate codebases.

The best way to enable mixed-precision training across multi-GPU scenarios is using Accelerate, which provides automatic mixed-precision support. This approach reduces boilerplate and minimizes errors by handling precision and device placement automatically within your existing PyTorch script.

You can configure Accelerate for single- or multi-GPU setups using its interactive configuration workflow. This process automatically handles device placement and launcher configuration, allowing you to apply the same script upgrades across different hardware configurations without manual wiring.

Yes, FSDP works with automatic device placement when using Accelerate for PyTorch distributed training. Accelerate wraps your model, optimizer, and dataloader to ensure compatibility with FSDP and other strategies while automatically managing device mapping and mixed precision.