Python extension language using accelerators.
Welcome to Pyccel
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:
- Convert a Python code (or project) into a Fortran or C code.
- 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):
- 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
- GFortran <https://gcc.gnu.org/fortran/>
- Intel® Fortran Compiler <https://software.intel.com/en-us/fortran-compilers>
- PGI Fortran <https://www.pgroup.com/index.htm>
For C it supports
- Gcc <https://gcc.gnu.org/>
- Intel® Compiler <https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/dpc-compiler.html>
- PGI <https://www.pgroup.com/index.htm>
In order to perform fast linear algebra calculations, Pyccel uses the following libraries:
- BLAS (Basic Linear Algebra Subprograms) <http://www.netlib.org/blas/>
- LAPACK (Linear Algebra PACKage) <http://www.netlib.org/lapack/>
Finally, Pyccel supports distributed-memory parallel programming through the Message Passing Interface (MPI) standard; hence it requires an MPI library like
- Open-MPI <https://www.open-mpi.org/>
- MPICH <https://www.mpich.org/>
- Intel® MPI Library <https://software.intel.com/en-us/mpi-library>
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/>.
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
Install all requirements using the DNF software package manager:
su dnf check-update dnf install gcc dnf install gfortran dnf install blas-devel lapack-devel dnf install openmpi-devel exit
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
Open git-bash as Administrator. Change default C compiler from M$ to mingw in Anaconda:
echo -e "[build]\ncompiler = mingw32" > /c/tools/Anaconda3/Lib/distutils/distutils.cfg
Download x64 BLAS and LAPACK DLLs from https://icl.cs.utk.edu/lapack-for-windows/lapack/:
WEB_ADDRESS=https://icl.cs.utk.edu/lapack-for-windows/libraries/VisualStudio/3.7.0/Dynamic-MINGW/Win64 LIBRARY_DIR=/c/ProgramData/chocolatey/lib/mingw/tools/install/mingw64/lib curl $WEB_ADDRESS/libblas.dll -o $LIBRARY_DIR/libblas.dll curl $WEB_ADDRESS/liblapack.dll -o $LIBRARY_DIR/liblapack.dll
Generate static MS C runtime library from corresponding dynamic link library:
cd "$LIBRARY_DIR" 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:
WEB_ADDRESS=https://github.com/microsoft/Microsoft-MPI/releases/download/v10.1.1 curl -L $WEB_ADDRESS/msmpisetup.exe -o msmpisetup.exe curl -L $WEB_ADDRESS/msmpisdk.msi -o msmpisdk.msi ./msmpisetup.exe msiexec //i msmpisdk.msi
At this point, close and reopen your terminal to refresh all environment variables!
In Administrator git-bash, generate mpi.mod for gfortran according to https://abhilashreddy.com/writing/3/mpi_instructions.html:
cd "$MSMPI_INC" 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.
Simply run, for a user-specific installation:
pip3 install --user pyccel
sudo pip3 install pyccel
for a system-wide installation.
git clone firstname.lastname@example.org:pyccel/pyccel.git cd pyccel pip3 install --user .
git clone email@example.com: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.
In order to run the unit tests and to get a coverage report, four 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 coverage
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).
- 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.
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 .
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