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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):
- numpy
- scipy
- mpi4py (not available yet)
- h5py (not available yet)
If you are eager to try Pyccel out, we recommend reading our quick-start guide
Table of contents
User Documentation
-
Supported libraries/APIs
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.
It is possible to use pyccel with anaconda but this is generally not advised as anaconda modifies paths used for finding executables, shared libraries and other objects. Support is provided for anaconda on linux/macos.
On Windows support is limited to examples which do not use external libraries. This is because we do not know of a way to reliably avoid DLL hell. As a result DLLs managed by conda are always loaded before DLLs related to the compiler.
Requirements
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.
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
sudo apt install libomp-dev libomp5
Linux Fedora-CentOS-RHEL
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
dnf install libgomp
exit
Similar commands work on Linux openSUSE, just replace dnf with zypper.
Mac OS X
On an Apple Macintosh machine we recommend using Homebrew:
brew update
brew install gcc
brew install openblas
brew install lapack
brew install open-mpi
brew install libomp
This requires that the Command Line Tools (CLT) for Xcode are installed.
Windows
Support for Windows is still experimental, and the installation of all requirements is more cumbersome. We recommend using Chocolatey to speed up the process, and we provide commands that work in a git-bash sh. 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/:
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.
Installation
From PyPi
Simply run, for a user-specific installation:
pip3 install --user pyccel
or:
sudo pip3 install pyccel
for a system-wide installation.
From sources
-
Standard mode:
git clone git@github.com:pyccel/pyccel.git cd pyccel pip3 install --user .
-
Development mode:
git clone git@github.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.
On a read-only system
If the folder where pyccel is saved is read only, it may be necessary to run an additional command after installation or updating:
sudo pyccel-init
This step is necessary in order to pickle header files. If this command is not run then pyccel will still run correctly but may be slower when using OpenMP or other supported external packages. A warning, reminding the user to execute this command, will be printed to the screen when pyccelizing files which rely on these packages if the pickling step has not been executed.
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
Testing
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 .
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