Accelerate
Run the same training script on any hardware, at any scale.
About
Accelerate is Hugging Face's library for hardware-agnostic training: write one PyTorch script and run it on CPU, single GPU, multi-GPU, TPU, or multi-node — with mixed precision, gradient accumulation, and sharding handled for you. 9.9k stars, Apache-2.0, 17.9M PyPI downloads/month — it's the invisible plumbing under transformers, diffusers, and most HF training. You rarely 'choose' Accelerate; you inherit it. But when your training script needs to scale from a laptop to a cluster without rewrites, this is the layer doing the work.
What it can do
- **Write once, run anywhere**: CPU, GPU, TPU, multi-node from one script
- **Mixed precision**: fp16/bf16 with a flag
- **Sharding**: FSDP and DeepSpeed integrations
- **Big-model inference**: device-map loading for oversized models
- **Ecosystem glue**: under transformers, diffusers, trl, peft
Setup
```bash
pip install accelerate
accelerate config # answer the hardware questions
```
```python
from accelerate import Accelerator
accelerator = Accelerator()
model, optimizer, loader = accelerator.prepare(model, optimizer, loader)
```
Then accelerate launch train.py. Docs: huggingface.co/docs/accelerate.
Prerequisites
Python 3.9+; PyTorch. `accelerate config` for your hardware.
Use cases
Works with
Trust score — 9/10
Scored 9/10 (verified tier, metrics-v1). Evidence: 9,911 GitHub stars (log-scaled vs catalog peers); blended with ~17,951,483 monthly package downloads; last push 0d ago; 5 releases/12mo; 98 open issues. Manual review: HF hardware-agnostic training (9.9k stars, 17.9M PyPI/mo) — invisible ecosystem plumbing; a dependency, not a product. License: Apache-2.0.
★ 9,911 GitHub stars
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 8.3/10
- Maintenance · recency, releases, issue load
- 8.4/10
- Openness · license + docs
- 10.0/10
- Manual review · sanity read + red-flag check
- 9.0/10
- · 9,911 GitHub stars (log-scaled vs catalog peers)
- · blended with ~17,951,483 monthly package downloads
- · last push 0d ago; 5 releases/12mo; 98 open issues
- · manual review: HF hardware-agnostic training (9.9k stars, 17.9M PyPI/mo) — invisible ecosystem plumbing; a dependency, not a product
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.