GPyTorch
A highly efficient, scalable Gaussian process library implemented in PyTorch with GPU acceleration and modular design.
What is GPyTorch?
GPyTorch is a Gaussian process library implemented in PyTorch, designed for creating scalable, flexible, and modular Gaussian process models. It solves the problem of inefficient GP inference by using numerical linear algebra techniques like preconditioned conjugate gradients, offering significant GPU acceleration over traditional Cholesky-based methods.
Machine learning researchers and practitioners working with Gaussian processes, especially those needing scalable inference, GPU acceleration, or integration with deep learning models in PyTorch.
Developers choose GPyTorch for its state-of-the-art scalable algorithms, efficient GPU utilization through MVM-based inference, and seamless integration with PyTorch's deep learning ecosystem, enabling hybrid modeling with minimal overhead.
Overview
A highly efficient implementation of Gaussian Processes in PyTorch
Use Cases
Best For
- Implementing scalable Gaussian process models with large datasets
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