gpboost 1.6.6


pip install gpboost

  Latest version

Released: Feb 11, 2026

Project Links

Meta
Maintainer: Fabio Sigrist
Requires Python: >=3.7

Classifiers

Development Status
  • 5 - Production/Stable

Intended Audience
  • Science/Research

License
  • OSI Approved :: Apache Software License

Natural Language
  • English

Operating System
  • MacOS
  • Microsoft :: Windows
  • POSIX
  • Unix

Programming Language
  • Python :: 3
  • Python :: 3.7
  • Python :: 3.8
  • Python :: 3.9
  • Python :: 3.10
  • Python :: 3.11
  • Python :: 3.12
  • Python :: 3.13

Topic
  • Scientific/Engineering :: Artificial Intelligence

GPBoost icon

GPBoost Python Package

License

This is the Python package implementation of the GPBoost library. See https://github.com/fabsig/GPBoost for more information on the modeling background and the software implementation.

Table of Contents

Examples and documentation

Installation

Before you install

Installation from PyPI using precompiled binaries

pip install gpboost -U

Requirements

  • For Windows users, VC runtime is needed if Visual Studio (2015 or newer) is not installed.

  • For Linux users, glibc >= 2.14 is required.

    • If you get an error message version `GLIBC_2.27' not found, you need to install from source.

    • In rare cases, when you get the OSError: libgomp.so.1: cannot open shared object file: No such file or directory error when importing GPBoost, you need to install the OpenMP runtime library separately (use your package manager and search for lib[g|i]omp for doing this).

  • For macOS users:

    • The library file in distribution wheels is built by the Apple Clang compiler. You need to install the OpenMP library. You can install the OpenMP library by the following command: brew install libomp.

    • If you have an arm64 Apple silicon processor (e.g., M1 or M2) and experience problems, try the following steps:

      • uninstall homebrew (in case you have migrated from an older non-arm64 Mac)
      • install homebrew (to make sure that you have an arm64 version of libomp)
      • install OpenMP (brew install libomp)
      • remove existing python environments and install Miniforge (brew install miniforge and conda init "$(basename "${SHELL}")")

Installation from source

Installation from source can be either done from PyPI or GitHub.

Requirements for installation from source

  • Installation from source requires that you have installed CMake.

  • For Linux users, glibc >= 2.14 is required.

    • In rare cases, you may need to install the OpenMP runtime library separately (use your package manager and search for lib[g|i]omp for doing this).
  • For macOS users, you can perform installation either with Apple Clang or gcc.

    • In case you prefer Apple Clang, you should install OpenMP (details for installation can be found in the Installation Guide) first and CMake version 3.16 or higher is required. Only Apple Clang version 8.1 or higher is supported.

    • In case you prefer gcc, you need to install it (details for installation can be found in the Installation Guide) and specify compilers by running export CXX=g++-7 CC=gcc-7 (replace "7" with the version of gcc installed on your machine) first.

  • For Windows users, Visual Studio (or VS Build Tools) is needed.

Installation from source from PyPI

pip install --no-binary :all: gpboost
Build with MinGW-w64 on Windows
pip install gpboost --install-option=--mingw

Installation from source from GitHub

git clone --recursive https://github.com/fabsig/GPBoost.git
cd GPBoost/python-package
# export CXX=g++-7 CC=gcc-7  # macOS users, if you decided to compile with gcc, don't forget to specify compilers (replace "7" with version of gcc installed on your machine)
python -m pip install .
Build with MinGW-w64 on Windows
python setup.py install --mingw

Build CUDA Version

python setup.py install --cudagp

For macOS and Windows users, the CUDA version is not supported.

CUDA library is needed: details for installation can be found in Installation Guide.

1.6.6 Feb 11, 2026
1.6.5 Jan 09, 2026
1.6.4.2 Nov 12, 2025
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1.1.0 Mar 17, 2023
1.0.1 Mar 10, 2023
0.8.2 Feb 17, 2023
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0.8.0.1 Dec 05, 2022
0.8.0 Dec 02, 2022
0.7.10 Nov 11, 2022
0.7.9 Aug 29, 2022
0.7.8.4 Aug 19, 2022
0.7.8.3 Aug 19, 2022
0.7.8 Jul 08, 2022
0.7.7 Jun 10, 2022
0.7.6.3 Jun 10, 2022
0.7.6.2 May 06, 2022
0.7.6.1 May 06, 2022
0.7.6 May 06, 2022
0.7.3 Mar 22, 2022
0.7.2 Feb 21, 2022
0.7.1.5 Feb 01, 2022
0.7.1.4 Feb 01, 2022
0.7.1.3 Feb 01, 2022
0.7.1.2 Feb 01, 2022
0.7.1.1 Feb 01, 2022
0.7.1 Jan 15, 2022
0.7.0 Dec 10, 2021
0.6.7 Aug 18, 2021
0.6.5 Jul 12, 2021
0.6.4 Jul 09, 2021
0.6.3 Jun 21, 2021
0.6.2 May 28, 2021
0.6.1 May 15, 2021
0.6.0 Apr 26, 2021
0.5.1 Mar 26, 2021
0.5.0 Mar 15, 2021
0.4.2 Mar 09, 2021
0.4.1 Feb 19, 2021
0.4.0.3 Feb 17, 2021
0.4.0.2 Feb 17, 2021
0.4.0.1 Feb 17, 2021
0.4.0 Feb 17, 2021
0.3.0 Jan 29, 2021
0.2.3 Nov 11, 2020
0.2.2 Sep 08, 2020
0.2.1 Aug 15, 2020
0.2.0 Jul 14, 2020
0.1.7 Jun 12, 2020
0.1.6 Jun 12, 2020
0.1.5 Jun 12, 2020
0.1.4 Jun 12, 2020
0.1.3 Jun 12, 2020
0.1.2 May 14, 2020
0.1.1 May 11, 2020
0.1.0 Apr 24, 2020
0.0.4 Apr 23, 2020
0.0.3 Apr 21, 2020
0.0.1 Apr 07, 2020
Extras:
Dependencies:
wheel
numpy
pandas
scipy
scikit-learn (!=0.22.0)
optuna