GPflow/GPflow

Health v2 · maintenance only

Adoption not part of the score

1921 stars · 432 forks observed · 2026-10-15

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

machine-learning math data-science machine-learning data-science python cross-platform gaussian-processes tensorflow tensorflow-probability variational-inference bayesian-statistics mcmc kernels stochastic-processes probabilistic-modeling algorithms gpu

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markdown · JSON · MCP: product_card(name="GPflow/GPflow")