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GPyTorch provides an efficient implementation of Gaussian processes using PyTorch.
About GPyTorch
GPyTorch is an open-source Python library for building Gaussian process models with PyTorch. It performs inference with numerical linear algebra methods such as preconditioned conjugate gradients and matrix-vector multiplication, and supports custom methods through a LinearOperator interface for kernel matrix multiplication.
It includes implementations of SKI/KISS-GP, stochastic Lanczos expansions, LOVE, SKIP, and stochastic variational deep kernel learning. It requires Python 3.10 or newer, PyTorch 2.4.1 or newer, and NumPy 2.0 or newer, and is MIT licensed.
Key features
- Builds Gaussian process models using PyTorch
- Performs inference with preconditioned conjugate gradients
- Supports custom methods via LinearOperator matrix multiplication
- Uses matrix-vector multiplication inference for GPU computing
- Includes SKI/KISS-GP, LOVE, SKIP and related methods
- Installs with pip, conda, or ArchLinux AUR package
Best for: Python developers and researchers building Gaussian process models with PyTorch.
Good for
- Open source under MIT license
- Uses matrix-vector multiplication inference to utilize GPU computing
- Includes implementations for SKI/KISS-GP, LOVE, SKIP and related methods
Watch out for
- Requires Python >= 3.10, PyTorch >= 2.4.1 and NumPy >= 2.0
- ArchLinux AUR package is described as experimental
GPyTorch at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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GPyTorch FAQ
How do I install GPyTorch?
Run pip install gpytorch or conda install gpytorch -c gpytorch.
What versions are required?
Python 3.10 or newer, PyTorch 2.4.1 or newer, and NumPy 2.0 or newer.
What license does it use?
GPyTorch is MIT licensed.
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