# SheffieldML/GPy

Gaussian processes framework in python

Repository: https://github.com/SheffieldML/GPy
Canonical: https://ross.abutalabs.com/products/gpy
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2026-08-08T21:57:05+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 96, longevity 100
- inputs: {"age_days": 5026, "days_push": 25, "days_rel": 26, "gap_med": 0.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2161, forks 570 (observed 2026-08-28T04:06:21.015591+00:00)

## What it is
GPy is a Python framework for Gaussian process modeling, providing kernels, regression, classification, and sparse GP methods. It is a research-oriented toolbox from the University of Sheffield for probabilistic machine learning with Gaussian processes.

## Use cases
- fit a gaussian process regression model in python
- model uncertainty with gaussian processes
- use custom kernels for GP regression
- sparse gaussian process for large datasets
- bayesian optimization surrogate modeling
- GP classification with probabilistic outputs

## When to choose
- you need a mature, well-documented Gaussian process library in Python
- you want flexible kernel composition and sparse GP approximations
- you are doing research on GP models and want extensible code

## When to avoid
- you need GPU-accelerated or very large-scale GP inference
- you prefer actively developed alternatives like GPyTorch
- you need general-purpose ML beyond Gaussian processes

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: gaussian-processes, kernels, regression, bayesian-inference, probabilistic-modeling, algorithms

## Member repositories
- SheffieldML/GPy (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:21.015591+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 2026-08-30T02:50:14.964527+00:00, confidence not recorded.
  - readme: https://github.com/SheffieldML/GPy (fetched 2026-08-28T04:06:21.015591+00:00, sha 758a5c3bf10c)
  - registry_pypi: https://pypi.org/pypi/gpy/json (fetched 2026-08-29T10:30:03.642401+00:00, sha 0fb2ebd9a44d)
- Data as of 2026-08-30T08:39:29.467469+00:00.