huggingface-accelerate

Distributed training across multiple GPUs is unified through a single Accelerate API that abstracts device placement and distributed logic, allowing your PyTorch code to run on CPU, single-GPU, or multi-node clusters with minimal modification.

Mixed-precision training supports diverse parallelism strategies including FSDP and DeepSpeed through a plugin-based system, allowing you to configure and customize these strategies within the same unified workflow rather than simultaneously merging their internal logic.

Scaling model training from single GPU to multi-node clusters is handled by configuring the Accelerate API, which automatically manages device placement, gradient accumulation, and distributed execution across your hardware without requiring manual infrastructure code.

Megatron-LM integration is managed through the optional MegatronLMPlugin, which must be configured alongside the accelerate package and PyTorch environment to enable large-scale model parallelism within your distributed training workflow.

Gradient accumulation logic breaks in multi-node environments when device placement and synchronization are managed manually, but the unified Accelerate API automatically handles these scaling operations to prevent such desynchronization issues.

Use the unified Accelerate API instead of native DDP scripts when you need to rapidly switch between CPU, single-GPU, and multi-node environments, or when integrating plugins like DeepSpeed and FSDP without maintaining separate infrastructure codebases.