{"library":"numpyro","title":"NumPyro","description":"NumPyro is a probabilistic programming library that leverages JAX for automatic differentiation, JIT compilation, and GPU/TPU acceleration. It allows users to build and infer Bayesian models with a flexible and composable API inspired by Pyro. NumPyro is currently at version 0.20.1 and maintains a regular release cadence, often releasing minor versions monthly or bi-monthly with new features, bug fixes, and performance improvements.","language":"python","status":"active","last_verified":"Sat May 16","install":{"commands":["pip install numpyro[cuda12_pip] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html","pip install numpyro[cpu]"],"cli":null},"imports":["import numpyro","import numpyro.distributions as dist","from numpyro.infer import MCMC, NUTS","import jax; key = jax.random.PRNGKey(0); key1, key2 = jax.random.split(key)"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import jax\nimport jax.numpy as jnp\nimport numpyro\nimport numpyro.distributions as dist\nfrom numpyro.infer import MCMC, NUTS\n\n# Optional: Uncomment to force CPU-only execution\n# jax.config.update(\"jax_platform_name\", \"cpu\")\n\ndef model(x, obs=None):\n    # Prior for intercept\n    a = numpyro.sample(\"a\", dist.Normal(0, 1))\n    # Prior for slope\n    b = numpyro.sample(\"b\", dist.Normal(0, 1))\n    # Prior for observation noise, must be positive\n    sigma = numpyro.sample(\"sigma\", dist.HalfCauchy(1))\n\n    # Linear model mean\n    mu = a + b * x\n\n    # Likelihood\n    numpyro.sample(\"obs\", dist.Normal(mu, sigma), obs=obs)\n\n# Generate some dummy data\nrng_key_data, rng_key_model = jax.random.split(jax.random.PRNGKey(0))\ntrue_a = 0.5\ntrue_b = 2.0\ntrue_sigma = 0.8\nN_samples = 100\nx_data = jax.random.normal(rng_key_data, (N_samples,))\ny_data = true_a + true_b * x_data + jax.random.normal(rng_key_data, (N_samples,)) * true_sigma\n\n# MCMC setup\nkernel = NUTS(model)\nmcmc = MCMC(\n    kernel,\n    num_warmup=500,\n    num_samples=1000,\n    num_chains=1,\n    progress_bar=False, # Set to True for interactive use\n    jit_model_args=True,\n)\n\n# Run MCMC\nmcmc.run(rng_key_model, x=x_data, obs=y_data)\nmcmc.print_summary()\n\n# # To get posterior samples:\n# samples = mcmc.get_samples()\n# # print(\"\\nSampled parameters:\", {k: v.shape for k, v in samples.items()})\n","lang":"python","description":"This quickstart demonstrates a basic Bayesian linear regression model using NumPyro with the NUTS sampler. It sets up a simple model, generates synthetic data, performs MCMC inference, and prints a summary of the posterior samples. It highlights proper `jax.random.PRNGKey` handling and passing data to the model.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-16","installed_version":"0.19.0","pypi_latest":"0.21.0","is_stale":true,"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":12.3,"avg_import_s":2.95,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"cpu","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine 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