huggingface-accelerate

To add distributed training to a PyTorch script, you can use a unified API that removes manual boilerplate by automatically handling device placement, mixed precision, and launching. This allows you to scale workloads across multiple GPUs or nodes with minimal code changes.

Yes, mixed precision training with FP16, BF16, and FP8 is fully supported. The system automatically handles precision handling during the distributed training process, allowing you to optimize performance without manually writing precision conversion logic.

Yes, it provides a unified configuration API that supports multiple parallelism backends including DeepSpeed, FSDP, Megatron, and DDP. This allows you to switch between distributed training strategies without rewriting your core PyTorch script.

The best way to scale a single-GPU PyTorch prototype to multi-node is applying a unified distributed wrapper that handles automatic device placement. You can then launch your training workload across multiple nodes using a single command.

No, manual device placement configuration is not required for multi-GPU training. The system satisfies automatic device placement internally, ensuring your tensors and models are correctly distributed across available hardware without explicit boilerplate.