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.
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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.
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.
