google-research/kauldron

What it solves

Kauldron addresses the difficulty of rapidly prototyping and iterating on machine learning models. It provides a modular framework that allows researchers to quickly swap components like datasets, models, and optimizers without rewriting the rest of their training pipeline.

How it works

Kauldron acts as the "glue" that connects various ML components. It is designed to be highly customizable, allowing users to overwrite specific parameters or sweep over different model architectures. It supports a wide range of data pipelines (such as TFDS, Grain, and SeqIO) and Flax models, ensuring that components remain self-contained and usable outside the framework.

Who it’s for

It is primarily designed for machine learning researchers who need a flexible, Colab-first workflow for fast iteration and prototyping.

Highlights

• High Modularity: Components can be used independently and any part of the training pipeline can be customized.
• Research Velocity: Includes example configurations to get started quickly and a Colab-first workflow.
• Integrated Tooling: Features built-in XM plots, a profiler, runtime shape checking, and post-mortem debugging.
• Broad Compatibility: Works with various datasets and Flax models.