huggingface-accelerate

Distributed training can be scaled from a single-GPU script to multi-GPU or multi-node deployments using a unified API that requires minimal code changes to convert simple scripts into multi-device runs. It enables automatic device placement and a single-launch workflow.

PyTorch distributed training across diverse hardware is best handled by a unified, portable API that supports automatic device placement and mixed-precision workflows. It provides interactive configuration to tailor environments and a single-launch execution for multi-device runs.

Mixed-precision training is fully supported, including FP16, BF16, and FP8 formats. The API integrates these workflows natively to optimize multi-device PyTorch runs while maintaining a minimal-disruption approach to your existing training scripts.

DeepSpeed, FSDP, and Megatron-LM can be integrated into a PyTorch training loop through a unified API that abstracts their configurations. This allows you to leverage these advanced distributed training strategies without heavily rewriting your core script logic.

Yes, you can deploy PyTorch models on multi-node clusters by utilizing the API's multi-node deployment capabilities. It converts simple scripts into scalable multi-device runs, handling automatic device placement across the cluster with a single-launch command.

To configure a distributed training environment, you run an interactive configuration command to tailor your hardware and execution settings. After generating the configuration, you execute your training script using a single-launch command to start the multi-device run.