accelerate
Enrichment pending🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
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Trust & integrity
Full report- Maintenance
- Very active (3d since push)
- As of today · Source: github_public_v1
- Provenance
- Not a fork · Organization account
- As of today · Source: github_public_v1
- Security (OSV)
- No lockfile
- As of today · Source: none
Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.
Backing
Company and funding context for Hugging Face. Display-only - not part of trust score or organic ranking.
- Company
- Hugging Face·GitHub org profile·today
- Employees
- 160·Wikidata (P1128 employees)·today
- Funding
- $235,000,000 (2023-08)·GraphCanon curated seed (public press)·today
- Commercial model
- OSS + managed cloud·GraphCanon curated seed·today
Overview
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Jul 11, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
This repository is tested on Python 3.8+ and PyTorch 1.10.0+Source link
Tags
README
Installation
This repository is tested on Python 3.8+ and PyTorch 1.10.0+
You should install 🤗 Accelerate in a virtual environment. If you're unfamiliar with Python virtual environments, check out the user guide.
First, create a virtual environment with the version of Python you're going to use and activate it.
Then, you will need to install PyTorch: refer to the official installation page regarding the specific install command for your platform. Then 🤗 Accelerate can be installed using pip as follows:
pip install accelerate