google-research/kauldron
Modular, scalable library to train ML models
This is a library designed to help machine learning researchers quickly build and experiment with new model architectures. It takes various components like datasets, models, and optimizers, and provides the framework to connect them for training. The output is a trained machine learning model, along with tools for performance analysis and debugging, enabling rapid iteration on research ideas. It's built for ML researchers and engineers who are constantly prototyping and evaluating new models.
220 stars. Available on PyPI.
Use this if you are an ML researcher needing to quickly prototype, train, and iterate on different machine learning models and architectures.
Not ideal if you are looking for a simple, high-level API for deploying pre-built models or performing standard data analysis tasks without deep model customization.
Stars
220
Forks
24
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Dependencies
30
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/google-research/kauldron"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
explosion/thinc
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
google-deepmind/optax
Optax is a gradient processing and optimization library for JAX.
patrick-kidger/diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable....
google/grain
Library for reading and processing ML training data.
patrick-kidger/equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/