Installation

Linux

This procedure has been tested on Ubuntu22.04 (Linux x86).

1. Open a terminal.

2. Download Miniconda by running:

   $ curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   

3. Install Miniconda by running:

   $ bash ~/Miniconda3-latest-Linux-x86_64.sh
   

   • Accept the license term.

   • Accept the installation folder.

   • Accept to initialize Miniconda3.

   Close the terminal and open a new one. (base) should appear at the beginning of the terminal line.

   To prevent conda’s base environment to be activated when a new terminal is open:

   $ conda config --set auto_activate_base false
   

4. Create a new Python3.9 environment thanks to Miniconda3:

   $ conda create --name pySBOenv python=3.9
   

5. Activate the new environment:

   $ conda activate pySBOenv
   

   When pySBOenv is activated, (pySBOenv) appears at the beginning of a terminal line.

6. Install Pygmo(>=2.19.0):

   (pySBOenv) $ conda config --add channels conda-forge
   (pySBOenv) $ conda config --set channel_priority strict
   (pySBOenv) $ conda install pygmo
   

7. Install the other dependencies, either one-by-one:

   (pySBOenv) $ pip install numpy>=1.24.2
   (pySBOenv) $ pip install mpi4py>=3.1.4
   (pySBOenv) $ pip install matplotlib>=3.7.0
   (pySBOenv) $ pip install pybnn>=0.0.5
   (pySBOenv) $ pip install gpytorch>=1.9.1
   (pySBOenv) $ pip install tensorflow-cpu>=2.11.0
   (pySBOenv) $ pip install scipy>=1.10.1
   (pySBOenv) $ pip install pyDOE>=0.3.8
   (pySBOenv) $ pip install pyKriging>=0.2.0
   (pySBOenv) $ pip install scikit_learn>=1.2.1
   (pySBOenv) $ pip install pyro-ppl>=1.8.4
   

   or by retrieving the requirement file available here and running:

   (pySBOenv) $ pip install -r requirements.txt
   

8. Check the dependencies have been properly installed by importing them in Python:

   (pySBOenv) $ python
   >>> import pygmo, numpy, mpi4py, matplotlib, pybnn, gpytorch
   >>> import tensorflow, scipy, pyDOE, pyKriging, sklearn, pyro
   

   If some error related to GLIBCXX version shows up, update libstdc++.so.6 by running:

   (pySBOenv) $ sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
   (pySBOenv) $ sudo apt update
   (pySBOenv) $ sudo apt upgrade libstdc++6
   

9. Download pySBO by running:

   (pySBOenv) $ wget https://github.com/GuillaumeBriffoteaux/pySBO/archive/refs/heads/main.zip
   

10. Extract from the archive:

    (pySBOenv) $ unzip main.zip
    

11. To test the installation, go to the pySBO-main/examples directory and run the parallel Surrogate-Assisted Evolutionary Algorithm:

    (pySBOenv) $ mpiexec -n 2 python SAEA.py
    

    Check the outputs directory.

Windows

This procedures has been tested on Windows10.

1. Install Miniconda3 by following the instructions given at:

   https://conda.io/projects/conda/en/latest/user-guide/install/index.html

   Once Miniconda3 is installed, open Anaconda Prompt (miniconda3).

2. Deactivate the base environment:

   (base) > conda deactivate
   

3. Create a new Python3.9 environment:

   > conda create --name pySBOenv python=3.9
   

4. Activate the new environment:

   > conda activate pySBOenv
   

   When pySBOenv is activated, (pySBOenv) appears at the beginning of the prompt line.

5. Install Pygmo(>=2.19.0):

   (pySBOenv) > conda config --add channels conda-forge
   (pySBOenv) > conda config --set channel_priority strict
   (pySBOenv) > conda install pygmo
   

6. Install the other dependencies:

   (pySBOenv) > pip install numpy>=1.24.2
   (pySBOenv) > pip install mpi4py>=3.1.4
   (pySBOenv) > pip install matplotlib>=3.7.0
   (pySBOenv) > pip install pybnn>=0.0.5
   (pySBOenv) > pip install gpytorch>=1.9.1
   (pySBOenv) > pip install tensorflow-cpu>=2.11.0
   (pySBOenv) > pip install scipy>=1.10.1
   (pySBOenv) > pip install pyDOE>=0.3.8
   (pySBOenv) > pip install pyKriging>=0.2.0
   (pySBOenv) > pip install scikit_learn>=1.2.1
   (pySBOenv) $ pip install pyro-ppl>=1.8.4
   

7. Check the dependencies have been properly installed by importing them in Python:

   (pySBOenv) > python
   >>> import pygmo, numpy, mpi4py, matplotlib, pybnn, gpytorch, tensorflow, scipy
   >>> import pyDOE, pyKriging, sklearn, pyro
   

8. Download MS MPI from:

   https://www.microsoft.com/en-us/download/details.aspx?id=57467

   and install it (by running msmpisetup.exe).

9. Add the MS MPI bin folder (by default C:\Program Files\Microsoft MPI\Bin\) to the %PATH% environment variable. Follow the following tutorial to edit the %PATH% environment variable in Windows.

   https://www.computerhope.com/issues/ch000549.htm

10. Download pySBO from

    https://github.com/GuillaumeBriffoteaux/pySBO/archive/refs/heads/main.zip

    and extract from the archive.

11. To test the installation, go to the pySBO-main/examples directory from the Anaconda Prompt (miniconda3). Then run:

    (pySBOenv) > mpiexec /np 2 python SAEA.py
    

    Check the outputs directory.