GPyReg Documentation

What is it?

GPyReg is a lightweight package for Gaussian process regression in Python. It was developed for use with PyVBMC, a Python package for efficient black-box Bayesian inference, but is usable as a standalone package.

Installation

GPyReg is available via pip and conda-forge:

python -m pip install gpyreg

or:

conda install --channel=conda-forge gpyreg

GPyReg requires Python version 3.9 or newer.

BLAS threading

The cost of GPyReg is dominated by dense linear algebra, mainly the Cholesky factorizations and triangular solves performed by NumPy and SciPy. The installed BLAS/LAPACK library parallelizes these operations across threads. The most efficient number of threads depends on the workload and the environment. A single thread often works best for small training sets, of a few hundred points or fewer, and avoids oversubscription when the calling application already runs work in parallel; several threads tend to pay off for larger training sets. Benchmark a representative workload rather than assuming a setting.

GPyReg does not set a thread policy, leaving the choice to the user. Depending on which library NumPy is built against, the thread count is read from OPENBLAS_NUM_THREADS, MKL_NUM_THREADS or OMP_NUM_THREADS. These variables take effect only when set in the environment before NumPy is imported, for example with OPENBLAS_NUM_THREADS=1 python my_script.py in a POSIX shell.

The threadpoolctl package changes the limits at runtime instead, for the duration of a with block:

from threadpoolctl import threadpool_limits

with threadpool_limits(limits=1):
    hyp, optimization, sampling = gp.fit(X, y)

The OpenBLAS usage notes and runtime variables describe thread control in more detail.

Documentation

The primary entry point for users is the GP Class, used to construct and fit Gaussian process models to data. More detailed information can be found in the links below:

Contents:

• Release notes
  • 1.3.1 (2026-09-23)
  • 1.3.0 (2026-09-23)
  • 1.2.1 (2026-09-14)
  • 1.2.0 (2026-09-13)
  • 1.1.0 (2026-09-05)
• Gaussian processes
  • gpyreg.gaussian_process
• Covariance functions
  • gpyreg.covariance_functions
• Isotropic covariance functions
  • gpyreg.isotropic_covariance_functions
• Mean functions
  • gpyreg.mean_functions
• Noise functions
  • gpyreg.noise_functions
• Slice sampling
  • gpyreg.slice_sample
• Random number generation
  • gpyreg.rng

Indices and tables

• Index

• Search Page

License and source

GPyReg is released under the terms of the BSD 3-Clause License. The source code is on GitHub.

Acknowledgments

GPyReg is developed by members (past and current) of the Machine and Human Intelligence Lab at the University of Helsinki and ELLIS Institute Finland. Development of GPyReg from version 1.1 onwards has been assisted by coding agents, including Anthropic’s Claude Fable 5.1 and OpenAI’s GPT-6 Astra. Work on the GPyReg package is supported by the Research Council of Finland (grants 356498 and 358980 to Luigi Acerbi) and its Flagship programme: Finnish Center for Artificial Intelligence FCAI.