---
id: gpytorch
version: "1.15.2"
license: MIT
license_treatment: permissive
maintenance: active
---

# gpytorch — An implementation of Gaussian Processes in Pytorch

License: permissive · Maintenance: active · Downloads: 1.2M/mo

## 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 above — verify before relying.

GPyTorch is a PyTorch-based Gaussian process library that enables scalable GP inference using numerical linear algebra techniques and GPU acceleration via matrix-vector multiplication operations.

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

pip install gpytorch
uv add gpytorch
poetry add gpytorch

## Installing gpytorch

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.

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())

Requires Python >= 3.10 and PyTorch >= 2.0; GPU support is optional but recommended for scalability.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 1.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

gaussian process pytorch, scalable GP inference, GPU accelerated gaussian processes, kernel matrix linear algebra, variational inference deep learning, gaussian-processes, gpu-acceleration, probabilistic-ml

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