nutpie
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Python wrapper for nuts-rs
I want to be able to run a python callback on each sample. The signature would be different than this: https://github.com/wd60622/nutpie/blob/5b881638d658fb8236d4b627209d7f034ee02050/python/nutpie/sample.py#L361-L363
Pardon my ignorance, but I cannot get nutpie to sample from PYMC's hurdle-gamma likelihood. I get "TypeError: The fgraph of ScalarLoop must be exclusively composed of scalar operations." Is this...
# Minimal example ```python import time import arviz as az import numpy as np import nutpie import pandas as pd import pymc as pm BETA = [1.0, -1.0, 2.0, -2.0]...
Introduces MLX as a backend option in compile_pymc_model, allowing gradient computation via MLX or Pytensor. Updates dependency groups to include MLX, extends internal functions to handle MLX mode, and adds...
Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 5. Release notes Sourced from actions/upload-artifact's releases. v5.0.0 What's Changed BREAKING CHANGE: this update supports Node v24.x. This is not a breaking change per-se but...
Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 5 to 6. Release notes Sourced from actions/download-artifact's releases. v6.0.0 What's Changed BREAKING CHANGE: this update supports Node v24.x. This is not a breaking change per-se but...
updates: - [github.com/astral-sh/ruff-pre-commit: v0.14.1 → v0.14.2](https://github.com/astral-sh/ruff-pre-commit/compare/v0.14.1...v0.14.2)
nutpie shows no progressbar anymore, if ipykernel 7.0.0 is installed. The progress updates come from a background thread, and updates to the repr_html of an object coming from a different...
I have a model that returns samples ok with low values of `tune`, but when I bump to high values, python crashes just as the sampling completes, with my OS...
``` compiled_jax = nutpie.compile_pymc_model(censored_model, backend="jax") trace = nutpie.sample( compiled_jax, draws=1000, tune=500, chains=4, ) ``` No progress bar displays when running this command in a databricks notebook in `.ipynb` or `.py`...