gpytorch
An implementation of Gaussian Processes in Pytorch
Decision gist · record as of 2026-08-14
Yes. GPyTorch is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively under MIT. It is well-suited for anyone building Gaussian process models in PyTorch, especially when GPU acceleration or integration with deep learning is desired. The library's modular design and algorithmic breadth make it a strong choice for both research and production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10 and PyTorch >= 2.0; GPU support is optional but recommended for scalability.
- Low friction installation with a pure Python wheel.
- The package is actively maintained with a recent release and depends on well-established libraries (scipy, scikit-learn, mpmath, linear_operator), all of which install without compilation barriers.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute GPyTorch with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-02-28 (167 days) · last repo commit 2026-07-10 · 3,906 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,159,768 downloads/mo, #4,286 on PyPI
Alternatives
Verify before relying
pip install gpytorch
import gpytorch
# Create a simple GP model by subclassing and composing kernels
model = gpytorch.models.ExactGP(train_x, train_y, likelihood)
model.mean_module = gpytorch.means.ConstantMean()
model.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())- Whether GPU acceleration is automatic or requires explicit configuration for typical workflows.
- Performance characteristics and scalability limits for different dataset sizes and kernel types.
- Availability and maturity of implementations for all advertised algorithmic advances (SKI/KISS-GP, LOVE, SKIP, stochastic variational, deep kernel learning).
What it is and what it does
GPyTorch is a Gaussian process library built on PyTorch that shifts GP inference away from Cholesky-based solvers toward preconditioned conjugate gradient and matrix-vector multiplication techniques. This design choice enables both GPU acceleration and modular composition of inference methods through its linear_operator interface. The library implements recent algorithmic advances in scalable GPs and integrates naturally with deep learning workflows, making it suitable for practitioners who need flexible, GPU-friendly GP models alongside neural network components.
The package depends on scipy, scikit-learn, mpmath, and linear_operator for its core functionality. It targets modern Python versions (3.10+) and PyTorch 2.0+, reflecting a focus on current-generation tooling. Installation is straightforward via pip or conda, and the library is actively maintained with regular releases.
Use it for
- Building scalable Gaussian process models for regression or classification on large datasets with GPU acceleration.
- Integrating Gaussian processes into deep learning pipelines using PyTorch's autograd and GPU infrastructure.
- Implementing custom GP inference methods by composing linear_operator for specialized kernel structures.
- Applying state-of-the-art variational inference or stochastic approximation techniques to reduce computational cost.
- Prototyping and experimenting with advanced GP algorithms without reimplementing inference from scratch.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
GPyTorch is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively under MIT. It is well-suited for anyone building Gaussian process models in PyTorch, especially when GPU acceleration or integration with deep learning is desired. The library's modular design and algorithmic breadth make it a strong choice for both research and production use.
Install
gpytorch on PyPI
Before you install
Low friction installation with a pure Python wheel. The package is actively maintained with a recent release and depends on well-established libraries (scipy, scikit-learn, mpmath, linear_operator), all of which install without compilation barriers.
Requires Python >= 3.10 and PyTorch >= 2.0; GPU support is optional but recommended for scalability.
License in practice
MIT license is permissive; you can use, modify, and distribute GPyTorch with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install gpytorch
import gpytorch
# Create a simple GP model by subclassing and composing kernels
model = gpytorch.models.ExactGP(train_x, train_y, likelihood)
model.mean_module = gpytorch.means.ConstantMean()
model.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())Verify before relying
- Whether GPU acceleration is automatic or requires explicit configuration for typical workflows.
- Performance characteristics and scalability limits for different dataset sizes and kernel types.
- Availability and maturity of implementations for all advertised algorithmic advances (SKI/KISS-GP, LOVE, SKIP, stochastic variational, deep kernel learning).
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesmpmathscikit-learnscipylinear_operator |
| Maintenance | Actively maintained 167 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,159,768 / month, #4,286 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: Python :: 3 |
Evidence: gpytorch-1.15.2-py3-none-any.whl
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