g-learn Documentation#

deploy-docs

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.

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

3.9

3.10

3.11

3.12

3.8

3.9

3.10

Linux

X86-64

CPU

build-linux

GPU

AARCH-64

CPU

GPU

macOS

X86-64

CPU

build-macos

GPU

ARM-64

CPU

GPU

Windows

X86-64

CPU

build-windows

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#

conda-downloads

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:

Docker#

docker-pull deploy-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:

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.

Tutorials#

binder

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

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 O(n) complexity.

Technical Notes#

tokei-2 languages

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

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:

License#

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.