huggingface-accelerate

You can run PyTorch distributed training across backends like DDP, DeepSpeed, and FSDP using a unified API that abstracts boilerplate and handles automatic device placement. This allows scalable training from a single machine to multi-node clusters seamlessly.

The best way to switch between DDP and DeepSpeed is using an interactive configuration tool that applies a single, consistent API. It abstracts backend-specific boilerplate, allowing you to change distributed training setups without altering your core PyTorch code.

Yes, accelerate works with Megatron-LM workflows and scales from a laptop to a multi-node cluster. It provides a unified API that supports automatic device placement and mixed-precision training for scalable PyTorch projects.

To enable automatic mixed precision training in PyTorch, you can use a unified interface that supports FP16, BF16, and FP8. It handles device placement and allows you to launch the training script with a single command.

Yes, you can launch multi-node PyTorch training from a single command. After interactively configuring your environment, a simple one-command launch workflow initiates distributed training across your specified backends.

No, you do not need to manually manage device placement for FSDP training. The unified API handles automatic device placement, abstracting boilerplate and streamlining the distributed training process across backends.