nutpie

library0.16.10pypypi✓ verified 79d ago

Nutpie is a Python library designed to sample Stan or PyMC models efficiently, leveraging JAX for high-performance computation. It provides an alternative MCMC sampler for probabilistic programming models, aiming for speed and robustness. The current version is 0.16.8, and it maintains a frequent release cadence, often with minor bug fixes, dependency updates, and feature enhancements.

ai-ml data

pip install nutpie

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · NUTPIE

nutpie

ai-mlpythonv0.16.10

Install

19.8s avg

Import

—

Disk

583MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.15.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 19.8s · import 0.000s · 566MB

583MB installed

● package 583MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

sample

✓ from nutpie import sample

✗ import nutpie; nutpie.sample(...)

This example demonstrates how to define a basic PyMC model and then use `nutpie.sample()` to perform MCMC sampling. The results are returned in an `arviz.InferenceData` object.

import pymc as pm import nutpie as np # Define a simple PyMC model with pm.Model() as model: # Priors mu = pm.Normal('mu', mu=0, sigma=1) sigma = pm.HalfNormal('sigma', sigma=1) # Likelihood obs = pm.Normal('obs', mu=mu, sigma=sigma, observed=[1.0, 2.0, 3.0]) # Sample the model using Nutpie print("Starting Nutpie sampling...") idata = np.sample(model) print("Sampling complete. InferenceData:\n", idata)

Debug

Known issues

gotchaOlder versions of Nutpie (pre-0.16.7) might encounter compatibility issues when sampling PyMC models that heavily utilize `pymc.dims`. Ensure you are on a recent version for full `pymc.dims` support.

fix

Upgrade Nutpie to version 0.16.7 or newer: `pip install --upgrade nutpie`

affects: <0.16.7

gotchaNutpie versions prior to 0.16.5 had a bug affecting compatibility with pandas 3.0 for string coordinates. If you use pandas 3.0 or later with models containing string coordinates, you might encounter errors.

fix

Upgrade Nutpie to version 0.16.5 or newer: `pip install --upgrade nutpie`

affects: <0.16.5

gotchaThe `mindepth` parameter when `check_turning=True` was misbehaving in versions before 0.16.3. This could lead to incorrect or inefficient sampling behavior under specific configurations.

fix

Upgrade Nutpie to version 0.16.3 or newer to ensure correct `mindepth` behavior: `pip install --upgrade nutpie`

affects: <0.16.3

breakingStarting from v0.16.0, step size jitter is enabled by default during NUTS sampling. This changes the default behavior, potentially leading to slightly different sampling paths compared to previous versions where it was disabled by default.

fix

If you rely on the previous behavior without step size jitter, you can explicitly disable it by passing appropriate arguments to `nutpie.sample()` (e.g., `init_strategy='advi_map'` or setting `step_size_jitter` to 0 if an option is exposed, refer to docs).

affects: >=0.16.0

Upgrade

Version history

0.16.10latest on PyPI · released May 11, 2026

Audit

Dependencies

pymcrequiredRequired for defining probabilistic models; Nutpie acts as an alternative sampler for PyMC models.

jaxrequiredUsed as the backend for high-performance computation and compilation.

Agent activity

15 hits · last 30 days

node

Amazon

Resources

githubhttps://github.com/pymc-devs/nutpie ↗

packagehttps://pypi.org/project/nutpie/ ↗

homepagehttps://pymc-devs.github.io/nutpie/ ↗