Parax v0.5: Parametric Modeling in JAX [P]

Reddit r/MachineLearning / 5/4/2026

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Key Points

Parax v0.5 is an update to a JAX-focused library for “parametric modeling,” aimed at making parameter definitions more structured and extensible.
The project has broadened its scope beyond earlier scientific use cases to support a wider range of JAX work, emphasizing a clean, extendable API.
Parax now follows an “opt-in” design rather than a more framework-like approach from previous versions, helping users adopt it selectively.
Key capabilities include derived/constrained parameters with metadata, computed PyTrees with callable parameterizations, abstract interfaces for fixed/bounded/probabilistic parameter structures, and filtering/manipulation utilities.
Documentation and basic examples are provided in the project site, positioning Parax as a potentially useful tool for developers working with JAX parameter pipelines.

Hi everyone!

Just sharing an update on my project Parax, which caters for "parametric modeling" in JAX.

Previously, Parax was more focused on scientific applications, however I've since generalized it to be a tool useful for any type of JAX work. It now has a strong focus on a clean, extandable API, as well as ensuring the library is entirely opt-in, as opposed to its previous versions which took a more framework-like approach.

Some of Parax's features:

Derived/constrained parameters with metadata
Computed PyTrees and callable parameterizations
Abstract interfaces for fixed, bounded, and probabilistic PyTrees and parameters
Filtering and manipulation tools

The documentation is available here along with some basic examples. Perhaps the package is of use to someone out there!

Cheers,
Gary

submitted by /u/gvcallen
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