# facebookresearch/fairscale

PyTorch extensions for high performance and large scale training.

Repository: https://github.com/facebookresearch/fairscale
Canonical: https://ross.abutalabs.com/products/fairscale
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2025-04-26T18:05:24+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 18, release rhythm 8, longevity 100
- inputs: {"age_days": 2248, "days_push": 494, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3407, forks 293 (observed 2026-08-28T04:08:03.167968+00:00)

## What it is
FairScale is a PyTorch extension library providing composable modules and APIs for high-performance, large-scale distributed training, including techniques like Fully Sharded Data Parallel (FSDP). Its FSDP implementation has been upstreamed to PyTorch, so this library now serves mainly as a historical reference and research playground for new scaling ideas.

## Use cases
- train large neural network models that don't fit on a single GPU
- shard model parameters and optimizer states across GPUs
- experiment with distributed training scaling techniques
- scale model training with limited GPU memory
- use model parallelism and pipeline parallelism in PyTorch

## When to choose
- you need to experiment with research-stage scaling techniques beyond what PyTorch ships
- you want composable distributed training modules with simple APIs
- you're studying how FSDP works or prototyping new sharding ideas

## When to avoid
- you just need FSDP in production - use the version built into modern PyTorch
- you need a actively developed library with new features
- you're not using PyTorch

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, llm-training, gpu-computing
- domain: deep-learning, machine-learning, large-language-models, gpu-computing
- platform: python, cross-platform
- tags: pytorch, distributed-training, fsdp, model-parallelism, data-parallelism, facebook-research, gpu, linux

## Member repositories
- facebookresearch/fairscale (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:03.167968+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:38:33.755868+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/fairscale (fetched 2026-08-28T04:08:03.167968+00:00, sha d9077eff63be)
- Data as of 2026-08-30T08:39:29.467469+00:00.