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)
Embedding models (e.g. BERT)
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.
Composer is part of the broader Machine Learning community, and we welcome any contributions, pull requests, and issues.