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GPErks

A Python library to (bene)fit Gaussian Process Emulators.


Information

Status: Actively developed

Type: Personal project

Development years: 2020 - Present

Authors: stelong, ShadowTemplate


Getting Started

Prerequisites

Installing

  1. Pull the source code from the project repository:
git clone https://github.com/stelong/GPErks.git
cd GPErks/
  1. (optional) Create a Python3 virtual environment:
python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools wheel
  1. Install PyTorch package first in order to satisfy your custom installation requirements (e.g., a CPU-only installation or a CUDA installation with a specific version that matches your machine NVIDIA drivers):

(CPU-only)

pip install torch --extra-index-url https://download.pytorch.org/whl/cpu

(CUDA 11.6)

pip install torch --extra-index-url https://download.pytorch.org/whl/cu116

Note: please check PyTorch website to customize your installation.

  1. Install GPErks library:
pip install .

Usage

Full documentation under construction. For the moment, please refer to the example notebooks and tutorial. The available notebooks are also provided as plain Python scripts.


Contributing

stelong and ShadowTemplate are the only maintainers. Any contribution is welcome!


License

This project is licensed under the MIT license. Please refer to the LICENSE file for details.


This README.md complies with this project template. Feel free to adopt it and reuse it.

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A Python library to (bene)fit Gaussian Process Emulators.

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