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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Supercharge Your Model Training
| Date | Stars |
|---|---|
| 2026-07-24 | 5488 |
| 2026-07-25 | 5489 |
| 2026-07-28 | 5489 |
| 2026-07-30 | 5489 |
| 2026-08-06 | 5489 |
Today
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growth rate 0.00%/day
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<p align="center">
<a href="https://github.com/mosaicml/composer#gh-light-mode-only" class="only-light">
<img src="./docs/source/_static/logo-light-mode.png" width="50%"/>
</a>
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<a href="https://github.com/mosaicml/composer#gh-dark-mode-only" class="only-dark">
<img src="./docs/source/_static/logo-dark-mode.png" width="50%"/>
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<h2><p align="center">Supercharge your Model Training</p></h2>
<h3><p align="center">Deep Learning Framework for Training at Scale</p></h3>
<h4><p align='center'>
<a href="https://www.mosaicml.com">[Website]</a>
- <a href="https://docs.mosaicml.com/projects/composer/en/stable/getting_started/installation.html">[Getting Started]</a>
- <a href="https://docs.mosaicml.com/projects/composer/">[Docs]</a>
- <a href="https://www.databricks.com/company/careers/open-positions?department=Mosaic%20AI&location=all">[We're Hiring!]</a>
</p></h4>
<p align="center">
<a href="https://pypi.org/project/mosaicml/">
<img alt="PyPi Version" src="https://img.shields.io/pypi/pyversions/mosaicml">
</a>
<a href="https://pypi.org/project/mosaicml/">
<img alt="PyPi Package Version" src="https://img.shields.io/pypi/v/mosaicml">
</a>
<a href="https://pepy.tech/project/mosaicml/">
<img alt="PyPi Downloads" src="https://static.pepy.tech/personalized-badge/mosaicml?period=month&units=international_system&left_color=grey&right_color=blue&left_text=Downloads/month">
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<a href="https://docs.mosaicml.com/projects/composer/en/stable/">
<img alt="Documentation" src="https://readthedocs.org/projects/composer/badge/?version=stable">
</a>
<a href="https://dub.sh/mcomm">
<img alt="Chat @ Slack" src="https://img.shields.io/badge/slack-chat-2eb67d.svg?logo=slack">
</a>
<a href="https://github.com/mosaicml/composer/blob/dev/LICENSE">
<img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green.svg?logo=slack">
</a>
</p>
<br />
# **👋 Welcome**
Composer is an open-source deep learning training library by [MosaicML](https://www.mosaicml.com/). Built on top of PyTorch, the Composer library makes it easier to implement distributed training workflows on large-scale clusters.
We built Composer to be **optimized for scalability and usability**, integrating best practices for efficient, multi-node training. By abstracting away low-level complexities like parallelism techniques, distributed data loading, and memory optimization, you can focus on training modern ML models and running experiments without slowing down.
We recommend using Composer to speedup your experimentation workflow if you’re training neural networks of any size, including:
- Large Language Models (LLMs)
- Diffusion models
- Embedding models (e.g. BERT)
- Transformer-based models
- Convolutional Neural Networks (CNNs)
Composer is heavily used by the MosaicML research team to train state-of-the-art models like MPT, and we open-sourced this library to enable the ML community to do the same. This framework is used by organizations in both the tech industry and the academic sphere and is continually updated with new features, bug fixes, and stability improvements for production workloads.
# **🔑 Key Features**
We designed Composer from the ground up for modern deep learning workloads. Gone are the days of AlexNet and ResNet, when state-of-the-art models could be trained on a couple of desktop GPUs. Today, developing the latest and greatest deep learning models often requires cluster-scale hardware — but with Composer’s help, you’ll hardly notice the difference.
The heart of Composer is our Trainer abstraction: a highly optimized PyTorch training loop designed to allow both you and youExcerpt of 19,589 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:83c6583a4fe9d7d9, topic:deep-learning, topic:neural-network, topic:pytorch