•1 min read•from Machine Learning
Parax v0.5: Parametric Modeling in JAX [P]
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Introducing Parax v0.5: a versatile tool for parametric modeling in JAX! This updated version broadens its scope beyond scientific applications, offering a clean, extendable API that enhances any JAX project. Parax now features derived and constrained parameters with metadata, computed PyTrees, and abstract interfaces for various parameter types. With a focus on user autonomy, the library is entirely opt-in, moving away from its previous framework-like model. Explore the documentation and examples to discover how Parax can streamline your JAX workflows. Cheers, Gary!
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
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