huggingface-accelerate

To add distributed training, define an Accelerator instance, prepare your model, optimizer, and dataloader, replace the loss backward call with accelerator.backward, and launch the script using accelerate launch. This abstracts device placement and process synchronization for multi-GPU and multi-node environments.

Using distributed training with plug-in integrations like DeepSpeed ZeRO or FSDP sharding allows you to scale large models by splitting states across devices. This manages memory pressure by abstracting distributed synchronization through a unified API without altering your core training logic.

Yes, mixed precision training is fully supported. You can enable FP16, BF16, and FP8 formats to achieve better performance and lower memory usage through automatic autocast and backward handling provided by the unified distributed API during model preparation.

Yes, you can run the same training code across single GPU, multi-GPU, and multi-node environments. The unified distributed API handles device placement, process launching, and synchronization so you do not need to rewrite core logic for different hardware setups.

No, you do not need to manually configure process orchestration. The Accelerator instance prepares your components and handles process launching and distributed synchronization automatically when using backend integrations like DeepSpeed ZeRO or FSDP sharding.