g-learn Documentation#

g-learn 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.

GitHub

PyPI

Anaconda Cloud

Docker Hub

Install

Tutorials

API reference

Publications

Supported Platforms#

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

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

Python wheels for g-learn for all supported platforms and versions in the above are available through PyPI and Anaconda Cloud. If you need g-learn on other platforms, architectures, and Python 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.}

Install#

Install with pip from PyPI:

pip install glearn

Install with conda from Anaconda Cloud:

conda install -c s-ameli glearn

For complete installation guide, see:

Install

Docker#

The docker image comes with a pre-installed g-learn, an NVIDIA graphic driver, and a compatible version of CUDA Toolkit libraries.

Pull docker image from Docker Hub:

docker pull sameli/glearn

For a complete guide, see:

Docker

GPU#

g-learn can run on CUDA-capable multi-GPU devices, which can be set up in several ways. Using the docker container is the easiest way to run g-learn on GPU devices. For a comprehensive guide, see:

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	✖	✖	✖	✔	✔	✔	✔	✔

API Reference#

Check the list of functions, classes, and modules of g-learn with their usage, options, and examples.

API Reference

Tutorials#

Launch an online interactive notebook with Binder. You can also explore the Jupyter notebooks below to get started using g-learn.

2D Example

Quick Start

Features#

Randomized algorithms using Hutchinson and stochastic Lanczos quadrature algorithms (see Overview)
Novel method to interpolate matrix functions. See Interpolation of Affine Matrix Functions.
Parallel processing both on shared memory and CUDA Capable multi-GPU devices.
Sparse covariance
Mixed covariance model, object
Automatic Relevance Determination (ARD)
Jacobian and Hessian based optimization
Learn hyperparameters in reduced space (profile likelihood)
Prediction in dual space with with \(\mathcal{O}(n)\) complexity.

Technical Notes#

Some notable implementation techniques used to develop g-learn are:

OS-independent customized dynamic loading of CUDA libraries.
Static dispatching enables executing g-learn with and without CUDA on the user’s machine with the same pre-compiled g-learn installation.
Completely GIL-free Cython implementation.
Providing manylinux wheels build upon customized docker images with CUDA support available on DockerHub:

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.

Publications#

For information on how to cite g-learn, publications, and software packages that used g-learn, see:

Publications
- How to Cite
- Publications/Software Using g-learn

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.