glearn 0.23.3

Gaussian Process for Machine Learning

glearn is a modular and high-performance Python package for machine learning using Gaussian process regression with novel algorithms capable of petascale computation on multi-GPU devices.

Links

• Documentation

• PyPI

• Anaconda

• Docker Hub

• Github

Install

Install with pip

pip install glearn

Install with conda

conda install -c s-ameli glearn

Docker Image

docker pull sameli/glearn

Supported Platforms

Successful installation and tests performed on the following operating systems, architectures, and Python and PyPy versions:

Platform	Arch	Device	Python Version	PyPy Version 1	Continuous Integration
3.9	3.10	3.11	3.12	3.8	3.9	3.10
Linux	X86-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU	✔	✔	✔	✔	✔	✔	✔
	AARCH-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU	✔	✔	✔	✔	✔	✔	✔
macOS	X86-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU	✖	✖	✖	✖	✖	✖	✖
	ARM-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU	✖	✖	✖	✖	✖	✖	✖
Windows	X86-64	CPU	✔	✔	✔	✔	✖	✖	✖
		GPU	✔	✔	✔	✔	✖	✖	✖

Python wheels for glearn for all supported platforms and versions in the above are available through PyPI and Anaconda Cloud. If you need glearn on other platforms, architectures, and Python or PyPy versions, raise an issue on GitHub and we build its Python Wheel for you.

^{1. Wheels for PyPy are exclusively available for installation through pip and cannot be installed using conda.}

^{2. Wheels for Windows on ARM-64 architecture are exclusively available for installation through pip and cannot be installed using conda.}

Supported GPU Architectures

glearn can run on CUDA-capable multi-GPU devices. Using the docker container is the easiest way to run glearn on GPU devices. The supported GPU micro-architectures and CUDA version are as follows:

Version \ Arch	Fermi	Kepler	Maxwell	Pascal	Volta	Turing	Ampere	Hopper
CUDA 9	✖	✖	✖	✖	✖	✖	✖	✖
CUDA 10	✖	✔	✔	✔	✔	✔	✔	✔
CUDA 11	✖	✖	✖	✔	✔	✔	✔	✔
CUDA 12	✖	✖	✖	✔	✔	✔	✔	✔

Documentation

See documentation, including:

• What This Packages Does?

• Comprehensive Installation Guide

• How to Work with Docker Container?

• How to Deploy on GPU Devices?

• API Reference

• Interactive Notebook Tutorials

• Publications

How to Contribute

We welcome contributions via GitHub’s pull request. If you do not feel comfortable modifying the code, we also welcome feature requests and bug reports as GitHub issues.

How to Cite

If you publish work that uses glearn, please consider citing the manuscripts available here.

License

This project uses a BSD 3-clause license, in hopes that it will be accessible to most projects. If you require a different license, please raise an issue and we will consider a dual license.