# pyro-ppl/numpyro

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Repository: https://github.com/pyro-ppl/numpyro
Canonical: https://ross.abutalabs.com/products/numpyro
Homepage: https://num.pyro.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pyro, jax, hmc, inference-algorithms, numpy, bayesian-inference, probabilistic-programming, mcmc
Last push: 2026-08-25T16:05:35+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 70, longevity 100
- inputs: {"age_days": 2758, "days_push": 8, "days_rel": 123, "gap_med": 58.0, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2746, forks 301 (observed 2026-08-28T04:07:16.778729+00:00)

## What it is
NumPyro is a lightweight probabilistic programming library providing a NumPy backend for Pyro, built on JAX for automatic differentiation and JIT compilation to GPU/TPU/CPU. It focuses on fast MCMC inference algorithms such as Hamiltonian Monte Carlo and the No U-Turn Sampler.

## Use cases
- fit bayesian models with mcmc in python
- run hamiltonian monte carlo fast on gpu
- port pyro models to a numpy/jax backend
- sample posterior distributions with NUTS
- do bayesian inference with jit-compiled kernels
- handle discrete latent variables in mcmc with MixedHMC
- scale hmc to large datasets with HMCECS

## When to choose
- you need fast, JIT-compiled MCMC/HMC inference in Python
- you want Pyro-style primitives with a NumPy/JAX backend
- you need GPU or TPU acceleration for Bayesian modeling
- you want a lightweight, hackable probabilistic programming substrate

## When to avoid
- you need PyTorch-based Pyro features or the full Pyro ecosystem
- you require variational inference or features not yet implemented in NumPyro
- you need a stable, frozen API - the project is under active development

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, math, sdk
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: probabilistic-programming, bayesian-inference, mcmc, jax, hmc, nuts, pyro, algorithms, gpu

## Member repositories
- pyro-ppl/numpyro (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:16.778729+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:19:03.368798+00:00, confidence not recorded.
  - readme: https://github.com/pyro-ppl/numpyro (fetched 2026-08-28T04:07:16.778729+00:00, sha 2a1a7d42cdb0)
  - registry_pypi: https://pypi.org/pypi/numpyro/json (fetched 2026-08-29T09:57:35.418063+00:00, sha 113b50c5646d)
- Data as of 2026-08-30T08:39:29.467469+00:00.