Hugging Face Accelerate is an open-source library that lets ordinary PyTorch training code run on several GPUs, TPUs or machines after adding a few lines.
In plain words
Code written for one GPU usually needs rewriting to use many, and each setup has its own rules. Accelerate hides those differences. You wrap your model, optimizer and data loader with its Accelerator object, and it handles the device and distributed details. It builds on PyTorch's own distributed tools and can switch on extras such as DeepSpeed, fully sharded data parallelism and mixed-precision training. You then start the script with its launch command.
A quick example
Imagine a training script that works on your laptop's single GPU. You add the Accelerator import, pass the model, optimizer and data loader through its prepare step, and replace the loss backward call with the Accelerator's version. The same file can now run on a machine with eight GPUs.
Where you'll meet it
- Scaling PyTorch training from one GPU to many.
- Using DeepSpeed or sharded training without rewriting code.
Related
Sources
- DocsAccelerate, Hugging Face documentation · read 29 Sept 2026
- Docshuggingface/accelerate, Hugging Face (GitHub) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.