# Kauldron: Modular, scalable library to train ML models

> Source: <https://github.com/google-research/kauldron>
> Published: 2026-10-25 19:53:33+00:00

Kauldron is a library for training machine learning models, optimized for
**research velocity** and **modularity**.

**Modularity**:

- All parts of Kauldron are self-contained, so can be used independently outside Kauldron.
- Use any dataset (TFDS, Grain, SeqIO, your custom pipeline), any (flax) model, any optimizer,... Kauldron provides the glue that link everything together.
- Everything can be customized and overwritten (e.g. sweep over models architecture, overwrite any inner layer parameter,...)

**Research velocity**:

- Everything should work out-of the box. The
[example configs](https://github.com/google-research/kauldron/tree/main/examples/mnist_autoencoder.py) can be used and customized as a starting point.
- Colab-first workflow for easy prototyping and fast iteration
- Polished user experience (integrated XM plots, profiler,
post-mortem debugging on borg, runtime shape checking, and many others...).
[Open an issue](https://github.com/google-research/kauldron/issues) ..

If Kauldron was helpful for a publication, please cite this repository:

```
@software{kauldron2025github,
  author = {Klaus Greff and Etienne Pot and Mehdi S. M. Sajjadi},
  title = {{Kauldron}: A neural network training framework, optimized for research velocity and modularity.},
  url = {https://github.com/google-research/kauldron},
  version = {1.3.0},
  year = {2025},
}
```

*This is not an officially supported Google product.*