tinygp
An extremely lightweight Gaussian Process library for Python built on JAX with GPU acceleration and automatic differentiation.
What is tinygp?
tinygp is an extremely lightweight Python library for building Gaussian Process models, built on top of JAX. It provides efficient GP modeling capabilities with GPU acceleration and automatic differentiation while maintaining a minimal codebase. The library focuses on core GP functionality with a clean, well-designed interface.
Data scientists, researchers, and machine learning practitioners who need efficient Gaussian Process modeling in Python, particularly those already working with JAX or requiring GPU acceleration.
Developers choose tinygp for its minimal dependencies, competitive performance, and clean API, offering essential GP functionality without the bloat of larger libraries while leveraging JAX's computational advantages.
Overview
The tiniest of Gaussian Process libraries
Use Cases
Best For
- Building lightweight Gaussian Process models with minimal dependencies
- GP applications requiring GPU acceleration through JAX
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