The main testing platform for PyOP2 is Ubuntu 12.04 64-bit with Python 2.7.3. Other UNIX-like systems may or may not work. Mac OS X 10.7 and 10.9 are also known to work. Microsoft Windows may work, but is not a supported platform.

For the impatient there is a script for the unattended installation of PyOP2 and its dependencies on a Ubuntu 12.04 or compatible platform. Only the sequential and OpenMP backends are covered at the moment.

Running with superuser privileges will install missing packages and Python dependencies will be installed system wide:

wget -O - https://github.com/OP2/PyOP2/raw/master/install.sh | sudo bash

Running without superuser privileges will instruct you which packages need to be installed. Python dependencies will be installed to the user site ~/.local:

wget -O - https://github.com/OP2/PyOP2/raw/master/install.sh | bash

In each case, PyOP2 will be cloned to subdirectories of the current directory.

After installation has completed and a rudimentary functionality check, the test suite is run. The script indicates whether all these steps have completed successfully and only in this case will exit with return code 0.

Only high-level progress updates are printed to screen. Most of the output is redirected to a log file pyop2_install.log. Please consult this log file in the case of errors. If you can't figure out the cause of discover a bug in the installation script, please report it.

This completes the quick start installation. More complete instructions follow for virtual machine and native installations.

A Vagrantfile is provided for automatic provisioning of a Ubuntu 12.04 64bit virtual machine with PyOP2 preinstalled. It requires VirtualBox 4.2 and Vagrant to be installed, which are available for Linux, Mac and Windows.

Creating and launching a virtual machine is a single command: run vagrant up to automatically download the base VM image, configure it for use with VirtualBox, boot the VM and install PyOP2 and all dependencies using the above install script.

PyOP2 requires a number of tools and libraries to be available:

• A C compiler (for example gcc or clang), make
• A Fortran compiler (for PETSc)
• MPI
• Blas and Lapack
• Git, Mercurial
• pip and the Python headers

The following dependencies are part of the Python subsystem:

• Cython >= 0.17
• decorator
• numpy >= 1.6
• networkx
• mpi4py >= 1.3.1

PETSc. We require very recent versions of PETSc so you will need to follow the specific instructions given below to install the right version.

• PETSc
• PETSc4py

Testing dependencies (optional, required to run the tests):

• pytest >= 2.3
• flake8 >= 2.1.0
• gmsh
• triangle

With the exception of the PETSc dependencies, these can be installed using the package management system of your OS, or via pip.

To install dependencies system-wide use sudo pip install ..., to install to a user site use pip install --user .... If you don't want PyOP2 or its dependencies interfering with your existing Python environment, consider creating a virtualenv.

On a Debian-based system (Ubuntu, Mint, etc.) install core packages:

sudo apt-get install -y build-essential python-dev git-core \
  mercurial python-pip libopenmpi-dev openmpi-bin libblas-dev \
  liblapack-dev gfortran

Install dependencies via pip:

sudo pip install "Cython>=0.17" decorator "numpy>=1.6" networkx "mpi4py>=1.3.1"

Additional Python 2.6 dependencies:

• argparse
• ordereddict

Install these via pip:

sudo pip install argparse ordereddict

We recommend using Homebrew as a package manager for the required packages on Mac OS systems. Obtaining a build environment for PyOP2 consists of the following:

1. Install Xcode. For OS X 10.9 (Mavericks) this is possible through the App Store. For earlier versions, try https://developer.apple.com/downloads (note that on OS X 10.7 (Lion) you will need to obtain Xcode 4.6 rather than Xcode 5)
2. If you did not install Xcode 5, you will need to additionally install the Xcode command line tools through the downloads section of Xcode's preferences
3. Install homebrew, following the instructions at http://brew.sh

4. Install an MPI library (PyOP2 is tested with openmpi):

   brew install openmpi

5. Install an up-to-date Python via homebrew:

   brew install python

   Note

   Do not follow the instructions to update pip, since they currently result in a broken pip installation (see Homebrew/legacy-homebrew#26900)

6. Install numpy via homebrew:

   brew tap homebrew/python
   brew install numpy

7. Install python dependencies via pip:

   pip install decorator
   pip install cython
   pip install mpi4py
   pip install pytest
   pip install flake8

PyOP2 uses petsc4py, the Python bindings for the PETSc linear algebra library and requires:

• an MPI implementation built with shared libraries
• A suitable very recent PETSc master branch built with shared libraries

If you have a suitable PETSc installed on your system, PETSC_DIR and PETSC_ARCH need to be set for the petsc4py installer to find it.

Then install PETSc via pip :

sudo PETSC_CONFIGURE_OPTIONS="--download-ctetgen --download-triangle --download-chaco" \
  pip install https://bitbucket.org/mapdes/petsc/get/firedrake.tar.bz2
unset PETSC_DIR
unset PETSC_ARCH

--with-threadcomm --with-openmp --with-pthreadclasses

If you built PETSc using pip, PETSC_DIR and PETSC_ARCH should be left unset when building petsc4py.

Install petsc4py via pip:

sudo pip install git+https://bitbucket.org/mapdes/petsc4py.git@firedrake#egg=petsc4py

If you have previously installed and older version of PETSc or petsc4py, pip might tell you that the requirements are already satisfied when running above commands. In that case, use pip install -U --no-deps to upgrade (--no-deps prevents also recursively upgrading any dependencies).

Dependencies:

• boost-python
• Cusp 0.3.1
• codepy >= 2013.1
• Jinja2
• mako
• pycparser >= 2.10
• pycuda >= 2013.1

The cusp library version 0.3.1 headers need to be in your (CUDA) include path.

Note: Using the trunk version of Cusp will not work, since revision f525d61 introduces a change that break backwards compatibility with CUDA 4.x.

Install dependencies via the package manager (Debian based systems):

sudo apt-get install libboost-python-dev python-jinja2 python-mako python-pycuda

Note: The version of pycparser available in the package repositories is too old, you will need to install it via pip, see below.

Install dependencies via pip:

sudo pip install codepy Jinja2 mako pycparser>=2.10

If a pycuda package is not available, it will be necessary to install it manually. Make sure nvcc is in your $PATH and libcuda.so in your $LIBRARY_PATH if in a non-standard location:

export CUDA_ROOT=/usr/local/cuda # change as appropriate 
git clone https://github.com/inducer/pycuda.git 
cd pycuda 
git submodule init 
git submodule update 
# libcuda.so is in a non-standard location on Ubuntu systems 
./configure.py --no-use-shipped-boost \
--cudadrv-lib-dir="/usr/lib/nvidia-current,${CUDA_ROOT}/lib,${CUDA_ROOT}/lib64" 
python setup.py build 
sudo python setup.py install 
sudo cp siteconf.py /etc/aksetup-defaults.py

Dependencies:

• Jinja2
• mako
• pycparser >= 2.10
• pyopencl >= 2012.1

pyopencl requires the OpenCL header CL/cl.h in a standard include path. On a Debian system, install it via the package manager:

sudo apt-get install opencl-headers

If you want to use OpenCL headers and/or libraries from a non-standard location you need to configure pyopencl manually:

export OPENCL_ROOT=/usr/local/opencl # change as appropriate 
git clone https://github.com/inducer/pyopencl.git 
cd pyopencl 
git submodule init 
git submodule update 
./configure.py --no-use-shipped-boost \
--cl-inc-dir=${OPENCL_ROOT}/include --cl-lib-dir=${OPENCL_ROOT}/lib 
python setup.py build 
sudo python setup.py install

Otherwise, install dependencies via pip:

sudo pip install Jinja2 mako pyopencl>=2012.1 pycparser>=2.10

Installing the Intel OpenCL toolkit (64bit systems only):

cd /tmp 
# install alien to convert the rpm to a deb package 
sudo apt-get install alien 
fakeroot wget http://registrationcenter.intel.com/irc_nas/2563/intel_sdk_for_ocl_applications_2012_x64.tgz
tar xzf intel_sdk_for_ocl_applications_2012_x64.tgz 
fakeroot alien *.rpm 
sudo dpkg -i --force-overwrite *.deb

The --force-overwrite option is necessary in order to resolve conflicts with the opencl-headers package (if installed).

Installing the AMD OpenCL toolkit (32bit and 64bit systems):

wget http://developer.amd.com/wordpress/media/2012/11/AMD-APP-SDK-v2.8-lnx64.tgz 
# on a 32bit system, instead 
wget http://developer.amd.com/wordpress/media/2012/11/AMD-APP-SDK-v2.8-lnx32.tgz 
tar xzf AMD-APP-SDK-v2.8-lnx*.tgz 
# Install to /usr/local instead of /opt 
sed -ie 's:/opt:/usr/local:g' default-install_lnx*.pl
sudo ./Install-AMD-APP.sh

PyOP2 allows initializing data structures using data stored in HDF5 files. To use this feature you need the optional dependency h5py.

On a Debian-based system, run:

sudo apt-get install libhdf5-mpi-dev python-h5py

Alternatively, if the HDF5 library is available, sudo pip install h5py.

Clone the PyOP2 repository:

git clone git://github.com/OP2/PyOP2.git

PyOP2 uses Cython extension modules, which need to be built in-place when using PyOP2 from the source tree:

python setup.py build_ext --inplace

When running PyOP2 from the source tree, make sure it is on your $PYTHONPATH:

export PYTHONPATH=/path/to/PyOP2:$PYTHONPATH

When installing PyOP2 via python setup.py install the extension modules will be built automatically and amending $PYTHONPATH is not necessary.

To make sure PyOP2 finds all its dependencies, create a file .env e.g. in your PyOP2 root directory and source it via . .env when using PyOP2. Use the template below, adjusting paths and removing definitions as necessary:

#PETSc installation, not necessary when PETSc was installed via pip
export PETSC_DIR=/path/to/petsc 
export PETSC_ARCH=linux-gnu-c-opt

#Add PyOP2 to PYTHONPATH
export PYTHONPATH=/path/to/PyOP2:$PYTHONPATH

Alternatively, package the configuration in an environment module.

PyOP2 unit tests use pytest >= 2.3. Install via package manager:

sudo apt-get install python-pytest

or pip:

sudo pip install "pytest>=2.3"

The code linting test uses flake8. Install via pip:

sudo pip install "flake8>=2.1.0"

If you install pytest and flake8 using pip --user, you should include the binary folder of your local site in your path by adding the following to ~/.bashrc or .env:

# Add pytest binaries to the path
export PATH=${PATH}:${HOME}/.local/bin

If all tests in our test suite pass, you should be good to go:

make test

This will run code linting and unit tests, attempting to run for all backends and skipping those for not available backends.

Start by verifying that PyOP2 picks up the "correct" dependencies, in particular if you have several versions of a Python package installed in different places on the system.

Run pydoc <module> to find out where a module/package is loaded from. To print the module search path, run:

python -c 'from pprint import pprint; import sys; pprint(sys.path)'

Run the tests as follows, to abort after the first failed test:

Start with the unit tests with the sequential backend :

py.test test/unit -vsx --tb=short --backend=sequential

With all the sequential tests passing, move on to the next backend in the same manner as required.