huggingface-accelerate

You can simplify distributed PyTorch training by using a unified API that wraps distributed backends like DeepSpeed and FSDP. This approach requires adding just four lines of code to enable multi-GPU setups without modifying your original script logic.

Mixed precision training in PyTorch is enabled by configuring the accelerator to use FP16, BF16, or FP8. This automatically handles precision casting during your training loop, significantly reducing memory requirements and accelerating training speed.

Yes, accelerate works with DeepSpeed, FSDP, and standard DDP for multi-node training. It provides a unified abstraction layer over these backends, allowing you to write a single training script that runs seamlessly across different distributed setups.

The easiest way to add gradient accumulation is through an accelerator wrapper that handles the synchronization automatically. This allows your single GPU script to simulate larger batch sizes without manual gradient scaling or complex loss accumulation logic.

Yes, you can run existing PyTorch scripts on a multi-GPU cluster with minimal code changes. By initializing an accelerator object and replacing standard model and optimizer definitions, the script automatically handles device placement and distributed execution.

You should use a unified API when you need to switch between distributed backends like DeepSpeed ZeRO-3 and FSDP without rewriting code. It abstracts complex configurations for large model training, including activation checkpointing and distributed optimizers.