•1 min read•from Machine Learning
Parax v0.7: Parametric Modeling in JAX [P]
Our take
Introducing Parax v0.7, a library designed for parametric modeling in JAX, merging the power of pure JAX PyTrees with object-oriented approaches like Equinox. This latest version boasts a refined API and enhanced documentation, featuring derived and constrained parameters with metadata, computed PyTrees, and abstract interfaces for various parameter types. New examples showcase bounded optimization using JAXopt and Bayesian sampling with BlackJAX, highlighting the library’s versatility. We invite you to explore Parax and share your feedback as you discover its capabilities. Cheers, Gary.
Hi everyone!
Parax is a library for "Parametric modeling" in JAX, attempting to bridge the approach between pure JAX PyTrees, and more object-orientated modeling approaches (e.g. using Equinox).
v0.7 has been released, featuring a more polished API as well as some detailed examples in the documentation.
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
Two new examples in the docs that show off these features
- Bounded optimization (JAXopt)
- Bayesian sampling (BlackJAX)
Perhaps the library is of use to someone, and feel free to leave any feedback!
Cheers,
Gary
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