Skip to content
Gaussian Process Optimization using GPy
Jupyter Notebook Python
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
GPyOpt Added x and y labels for plotted graphs (#263) Aug 20, 2019
docs Updated docs Mar 26, 2018
examples update six-hump camel example Jun 29, 2016
manual Fixed a few mishaps between the text and the code Apr 8, 2019
.gitignore Experimental implementation of entropy search acqusition May 22, 2018
AUTHORS.txt Released version 1.2.0 Oct 20, 2017
LICENSE.txt collection of notebooks added Mar 13, 2015 added acquisition optimization with CMA-ES Jul 20, 2015 Updated docs link Mar 27, 2018 finish new interface Jun 24, 2016
requirements.txt Experimental implementation of entropy search acqusition May 22, 2018
setup.cfg update setup.cfg Jun 29, 2016 Updated version May 22, 2018 remive show-skipped Jun 24, 2016


Gaussian process optimization using GPy. Performs global optimization with different acquisition functions. Among other functionalities, it is possible to use GPyOpt to optimize physical experiments (sequentially or in batches) and tune the parameters of Machine Learning algorithms. It is able to handle large data sets via sparse Gaussian process models.

licence develstat covdevel Research software impact


  author =   {The GPyOpt authors},
  title =    {{GPyOpt}: A Bayesian Optimization framework in python},
  howpublished = {\url{}},
  year = {2016}

Getting started

Installing with pip

The simplest way to install GPyOpt is using pip. ubuntu users can do:

    sudo apt-get install python-pip
    pip install gpyopt

If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH.

    git clone
    cd GPyOpt
    python develop


  • GPy
  • paramz
  • numpy
  • scipy
  • matplotlib
  • DIRECT (optional)
  • cma (optional)
  • pyDOE (optional)
  • sobol_seq (optional)

You can install dependencies by running:

pip install -r requirements.txt

Funding Acknowledgements

  • BBSRC Project No BB/K011197/1 "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"

  • See GPy funding Acknowledgements

You can’t perform that action at this time.