dfm/tinygp

The tiniest of Gaussian Process libraries

What it solves

It provides a lightweight, fast, and efficient way to implement Gaussian Process (GP) models in Python, avoiding the overhead of heavier libraries while maintaining high performance.

How it works

Built on top of JAX, the library leverages JAX's capabilities for automatic differentiation and GPU acceleration to speed up the computation of GP models.

Who it’s for

Researchers and developers who need a minimal yet powerful Gaussian Process library that integrates seamlessly with the JAX ecosystem.

Highlights

  • GPU acceleration support
  • Automatic differentiation
  • Lightweight and minimal interface
  • High performance as evidenced by benchmarks

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