huggingface-accelerate

You can scale PyTorch training across multiple GPUs by using a unified distributed API that prepares your model, optimizer, and dataloader once. This approach allows the same script to run on single or multi-GPU setups without modifying your workflow.

The best way to run mixed-precision training with FSDP and DeepSpeed is to use a unified API that supports both backends. You configure the launcher and sharded optimization settings once, and the API handles the backend-specific execution automatically.

Yes, you can use a single PyTorch script for both single GPU and multi-node distributed training. By relying on automatic device placement and a unified API, the script scales from a prototype to a multi-node fine-tune without code changes.

Yes, Hugging Face Accelerate supports Megatron-LM for large language model workloads. It integrates Megatron-LM alongside DDP, DeepSpeed, and FSDP within its unified distributed API to manage large-scale training scenarios efficiently.

You configure gradient accumulation and checkpointing for distributed training by enabling these features within the unified API's performance controls. This manages memory constraints and optimizes throughput for large models during execution.