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Kauldron is a ML research library optimized for quick iteration and modularity.

Project description

kauldron

Unittests PyPI version Documentation Status

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 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..

Citing Kauldron

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.

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