Installation

Regular installation

The following instructions assume that the operating system is Ubuntu. Run the corresponding commands in your flavor of Linux to install.

Dependencies (last tested)

  • Python 3.4+ (3.6.1)
  • Numpy 1.11.1+ (1.13.1)
  • SciPy 0.17.1+ (0.19.1)
  • SWIG 3.0.8+ (3.0.10)
  • NVCC 8.0
    • gcc 5.4.0
  • PyCUDA 2017.1.1
  • matplotlib 1.5.1+ (2.0.2) (optional, for post-processing only)

Python and Numpy

To install the specific version of these packages we recommend using either conda or pip.

To create a new environment for using PyGBe with conda you can do the following:

conda create -n pygbe python=3.6 numpy scipy swig matplotlib
source activate pygbe

and then proceed with the rest of the installation instructions (although note that if you do this, swig is already installed.

SWIG

To install SWIG we recommend using either conda, your distribution package manager or SWIG’s website.

NVCC

Download and install the CUDA Toolkit.

PyCUDA

PyCUDA must be installed from source. Follow the instructions on the PyCUDA website. We summarize the commands to install PyCUDA on Ubuntu here:

> cd $HOME
> mkdir src
> cd src
> wget https://github.com/inducer/pycuda/archive/v2016.1.2.tar.gz
> tar -xvzf pycuda-2016.1.2.tar.gz
> cd pycuda-2016.1.2
> python configure.py --cuda-root=/usr/local/cuda
> make
> sudo make install

If you are not installing PyCUDA systemwide, do not use sudo to install and simply run

> make install

as the final command.

Test the installation by running the following:

> cd test
> python test_driver.py

Installing PyGBe

Create a clone of the repository on your machine:

> cd $HOME/src
> git clone https://github.com/barbagroup/pygbe.git
> cd pygbe
> python setup.py install clean

If you are installing PyGBe systemwide (if you installed PyCUDA systemwide), then use sudo on the install command

> sudo python setup.py install clean

PyGBe has been run and tested on Ubuntu 12.04, 13.10, 15.04 and 16.04.

Installation using Docker

Requirements