# GPflow/GPflow

Gaussian processes in TensorFlow

Repository: https://github.com/GPflow/GPflow
Canonical: https://ross.abutalabs.com/products/gpflow
Language: Python
License: Apache-2.0
License Family: permissive
Topics: gaussian-processes, tensorflow, gpflow, machine-learning, variational-inference, bayesian-statistics, markov-chain-monte-carlo, stochastic-processes, deep-learning, ml, gp
Last push: 2026-08-10T09:43:42+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 89, longevity 100
- inputs: {"age_days": 3884, "days_push": 23, "days_rel": 78, "gap_med": 13, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1916, forks 432 (observed 2026-08-28T04:05:53.785708+00:00)

## What it is
GPflow is a Python library for building Gaussian process models on top of TensorFlow 2 and TensorFlow Probability. It implements modern Gaussian process inference with composable kernels and likelihoods, with GPU acceleration support.

## Use cases
- fit gaussian process regression models in python
- build gp models with custom kernels and likelihoods
- run variational inference for sparse gaussian processes
- do bayesian optimization with gaussian processes
- train gp models on gpu with tensorflow
- perform mcmc over gp hyperparameters
- model uncertainty with probabilistic machine learning

## When to choose
- you need gaussian process models with composable kernels and likelihoods
- you want GPU-accelerated gp inference via TensorFlow
- you need variational inference or MCMC for gp models
- you want an actively maintained, well-documented gp library in Python

## When to avoid
- you need a lightweight gp library without a TensorFlow dependency
- you work outside the Python/TensorFlow ecosystem
- you need deep learning models rather than probabilistic gp models

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, math, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: gaussian-processes, tensorflow, tensorflow-probability, variational-inference, bayesian-statistics, mcmc, kernels, stochastic-processes, probabilistic-modeling, algorithms, gpu

## Member repositories
- GPflow/GPflow (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.785708+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 2026-08-30T03:10:09.981649+00:00, confidence not recorded.
  - readme: https://github.com/GPflow/GPflow (fetched 2026-08-28T04:05:53.785708+00:00, sha 1e1d396be1de)
  - registry_pypi: https://pypi.org/pypi/gpflow/json (fetched 2026-08-29T10:49:30.043698+00:00, sha 6588a057d1f7)
- Data as of 2026-08-30T08:39:29.467469+00:00.