Welcome to Pyccel

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Pyccel stands for Python extension language using accelerators.

The aim of Pyccel is to provide a simple way to generate automatically, parallel low level code. The main uses would be:

  1. Convert a Python code (or project) into a Fortran or C code.
  2. Accelerate Python functions by converting them to Fortran or C functions.

Pyccel can be viewed as:

  • Python-to-Fortran/C converter
  • a compiler for a Domain Specific Language with Python syntax

Pyccel comes with a selection of extensions allowing you to convert calls to some specific python packages to Fortran/C. The following packages will be covered (partially):

  • numpy
  • scipy
  • mpi4py
  • h5py (not available yet)

If you are eager to try Pyccel out, we recommend reading our quick-start guide!

Pyccel Installation Methods

Pyccel can be installed on virtually any machine that provides Python 3, the pip package manager, a C/Fortran compiler, and an Internet connection. Some advanced features of Pyccel require additional non-Python libraries to be installed, for which we provide detailed instructions below.

Alternatively, Pyccel can be deployed through a Linux Docker image that contains all dependencies, and which can be setup with any version of Pyccel. For more information, please read the section on Pyccel container images.


First of all, Pyccel requires a working Fortran/C compiler

For Fortran it supports

For C it supports

In order to perform fast linear algebra calculations, Pyccel uses the following libraries:

Finally, Pyccel supports distributed-memory parallel programming through the Message Passing Interface (MPI) standard; hence it requires an MPI library like

We recommend using GFortran/GCC and Open-MPI.

Pyccel also depends on several Python3 packages, which are automatically downloaded by pip, the Python Package Installer, during the installation process. In addition to these, unit tests require the scipy, mpi4py, pytest and coverage packages, while building the documentation requires Sphinx <http://www.sphinx-doc.org/>.

Linux Debian/Ubuntu/Mint

To install all requirements on a Linux Ubuntu machine, just use APT, the Advanced Package Tool:

sudo apt update
sudo apt install gcc
sudo apt install gfortran
sudo apt install libblas-dev liblapack-dev
sudo apt install libopenmpi-dev openmpi-bin

Linux Fedora/CentOS/RHEL

Install all requirements using the DNF software package manager:

dnf check-update
dnf install gcc
dnf install gfortran
dnf install blas-devel lapack-devel
dnf install openmpi-devel

Similar commands work on Linux openSUSE, just replace dnf with zypper.

Mac OS X

On an Apple Macintosh machine we recommend using Homebrew <https://brew.sh/>:

brew update
brew install gcc
brew install openblas
brew install lapack
brew install open-mpi

This requires that the Command Line Tools (CLT) for Xcode are installed.


Support for Windows is still experimental, and the installation of all requirements is more cumbersome. We recommend using Chocolatey <https://chocolatey.org/> to speed up the process, and we provide commands that work in a git-bash shell. In an Administrator prompt install git-bash (if needed), a Python3 Anaconda distribution, and a GCC compiler:

choco install git
choco install anaconda3
choco install mingw

Download x64 BLAS and LAPACK DLLs from https://icl.cs.utk.edu/lapack-for-windows/lapack/:

curl $WEB_ADDRESS/libblas.dll -o $LIBRARY_DIR/libblas.dll
curl $WEB_ADDRESS/liblapack.dll -o $LIBRARY_DIR/liblapack.dll

Generate static MS C runtime library from corresponding dynamic link library:

cp $SYSTEMROOT/SysWOW64/vcruntime140.dll .
gendef vcruntime140.dll
dlltool -d vcruntime140.def -l libmsvcr140.a -D vcruntime140.dll
cd -

Download MS MPI runtime and SDK, then install MPI:

curl -L $WEB_ADDRESS/msmpisetup.exe -o msmpisetup.exe
curl -L $WEB_ADDRESS/msmpisdk.msi -o msmpisdk.msi
msiexec //i msmpisdk.msi

At this point, close and reopen your terminal to refresh all environment variables!

In Administrator git-bash, generate mpi.mod for gfortran according to https://abhilashreddy.com/writing/3/mpi_instructions.html:

sed -i 's/mpifptr.h/x64\/mpifptr.h/g' mpi.f90
sed -i 's/mpifptr.h/x64\/mpifptr.h/g' mpif.h
gfortran -c -D_WIN64 -D INT_PTR_KIND\(\)=8 -fno-range-check mpi.f90
cd -

Generate static libmsmpi.a from msmpi.dll:

cd "$MSMPI_LIB64"
cp $SYSTEMROOT/SysWOW64/msmpi.dll .
gendef msmpi.dll
dlltool -d msmpi.def -l libmsmpi.a -D msmpi.dll
cd -

Before installing Pyccel and using it, the Anaconda environment should be activated with:

source /c/tools/Anaconda3/etc/profile.d/conda.sh
conda activate

On Windows and/or Anaconda Python, use pip instead of pip3 for the Installation of pyccel below.


From PyPi

Simply run, for a user-specific installation:

pip3 install --user pyccel


sudo pip3 install pyccel

for a system-wide installation.

From sources

  • Standard mode:

    git clone [email protected]:pyccel/pyccel.git
    cd pyccel
    pip3 install --user .
  • Development mode:

    git clone [email protected]:pyccel/pyccel.git
    cd pyccel
    pip3 install --user -e .

this will install a python library pyccel and a binary called pyccel. Any required Python packages will be installed automatically from PyPI.

Additional packages

In order to run the unit tests and to get a coverage report, a few additional Python packages should be installed::

pip3 install --user scipy
pip3 install --user mpi4py
pip3 install --user tblib
pip3 install --user pytest
pip3 install --user astunparse
pip3 install --user coverage

Most of the unit tests can also be run in parallel. This can be done by installing one additional package:

pip3 install --user pytest-xdist


To test your Pyccel installation please run the script tests/run_tests_py3.sh (Unix), or tests/run_tests.bat (Windows).

Continuous testing runs on github actions: <https://github.com/pyccel/pyccel/actions?query=branch%3Amaster>

Pyccel Container Images

Pyccel container images are available through both Docker Hub (docker.io) and the GitHub Container Registry (ghcr.io).

The images:

  • are based on ubuntu:latest
  • use distro packaged python3, gcc, gfortran, blas and openmpi
  • support all pyccel releases except the legacy “0.1”

Image tags match pyccel releases.

In order to implement your pyccel accelerated code, you can use a host based volume during the pyccel container creation.

For example:

docker pull pyccel/pyccel:v1.0.0
docker run -it -v $PWD:/data:rw  pyccel/pyccel:v1.0.0 bash

If you are using SELinux, you will need to set the right context for your host based volume. Alternatively you may have docker or podman set the context using -v $PWD:/data:rwz instead of -v $PWD:/data:rw .