# mosaicml/composer

Supercharge Your Model Training

Repository: https://github.com/mosaicml/composer
Canonical: https://ross.abutalabs.com/products/mosaicml-composer
Homepage: http://docs.mosaicml.com
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, pytorch, neural-networks, ml-systems, ml-efficiency, ml-training, machine-learning, neural-network
Last push: 2026-04-29T18:58:33+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 79, release rhythm 28, longevity 100
- inputs: {"age_days": 1787, "days_push": 126, "days_rel": 404, "gap_med": 31, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5495, forks 465 (observed 2026-08-28T04:09:19.862342+00:00)

## What it is
Composer is an open-source PyTorch-based deep learning training library by MosaicML (now Databricks) for training neural networks faster and at lower cost. It abstracts distributed training, parallelism, data loading, and memory optimization for large-scale multi-node GPU clusters.

## Use cases
- train large language models on multi-node GPU clusters
- speed up PyTorch training of transformer models
- fine-tune BERT-style embedding models efficiently
- train diffusion models at scale
- run distributed CNN training experiments faster
- reduce GPU training costs for deep learning workloads

## When to choose
- you train neural networks on multi-GPU or multi-node clusters
- you want distributed training best practices without writing parallelism code yourself
- you are training LLMs, diffusion models, or transformers in PyTorch

## When to avoid
- you need a lightweight single-GPU training loop with minimal dependencies
- your project uses TensorFlow or JAX instead of PyTorch
- you need inference or serving rather than training

## Facets
- artifact type: library
- maturity: active
- function: deep-learning, llm-training, machine-learning, gpu-computing
- domain: deep-learning, machine-learning, large-language-models, gpu-computing
- platform: python, cloud
- tags: pytorch, distributed-training, training-efficiency, mosaicml, neural-networks, gpu, linux, docker

## Member repositories
- mosaicml/composer (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:19.862342+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 2026-08-29T17:56:19.670665+00:00, confidence not recorded.
  - readme: https://github.com/mosaicml/composer (fetched 2026-08-28T04:09:19.862342+00:00, sha 002f215e962a)
  - homepage: http://docs.mosaicml.com (fetched 2026-08-29T08:51:32.903108+00:00, sha f938805a13f5)
  - site_page: https://docs.mosaicml.com/en/latest/faq.html (fetched 2026-08-29T08:51:32.911952+00:00, sha baef9b2db5a2)
  - registry_pypi: https://pypi.org/pypi/composer/json (fetched 2026-08-29T08:51:32.913765+00:00, sha 805d3671e154)
- Data as of 2026-08-30T08:39:29.467469+00:00.