{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"NumPyro is a probabilistic programming library that uses JAX for automatic differentiation and JIT compilation, enabling Bayesian inference with MCMC and variational inference algorithms on CPU, GPU, and TPU.","skillfed_tags":["bayesian-inference","jax-accelerated","mcmc-sampling"],"use_cases":["Run MCMC inference on hierarchical Bayesian models with GPU acceleration via JAX JIT compilation.","Implement variational inference for models with discrete and continuous latent variables using ADVI.","Build custom inference algorithms by composing effect handlers and Pyro primitives.","Perform Bayesian data analysis with standard distributions and constraints similar to PyTorch's API.","Accelerate Hamiltonian Monte Carlo by compiling the entire verlet integrator and tree-building stage."],"what_it_does":"NumPyro is a lightweight probabilistic programming library that brings Pyro's API to JAX, enabling fast Bayesian inference through automatic differentiation and JIT compilation. It provides MCMC samplers (including the No-U-Turn Sampler and Hamiltonian Monte Carlo variants), variational inference with flexible guides, a comprehensive distribution library, and effect handlers for building custom inference algorithms.\n\nThe library is designed for users building hierarchical Bayesian models, performing posterior inference, or exploring probabilistic programming on modern hardware. It relies on jax, jaxlib, numpy, multipledispatch, and tqdm. The package is actively developed but explicitly warns of potential API changes as the design evolves.","worth_installing":"Yes, if you need probabilistic programming with GPU/TPU acceleration and are comfortable with an actively-developed library that may change its API. The low install friction, active maintenance, permissive license, and strong maintenance signal (recent release, 2736 stars) make it a solid choice for Bayesian inference workflows. Not recommended if you require API stability or are new to probabilistic programming."},"id":"numpyro","links":{"html":"https://skillfed.io/packages/numpyro","md":"https://skillfed.io/packages/numpyro.md","pypi":"https://pypi.org/project/numpyro/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-02","license_spdx":null,"license_treatment":"permissive","name":"numpyro","python_support":"supports_current","summary":"Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU."},"popularity":{"monthly_downloads":777898,"position":5087,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.21.0"}