Installation

Wheel packages

The mpi4py project builds and publishes binary wheels able to run in a variety of:

  • operating systems: Linux, macOS, Windows;

  • processor architectures: AMD64, ARM64;

  • MPI implementations: MPICH, Open MPI, MVAPICH, Intel MPI, HPE Cray MPICH, Microsoft MPI;

  • Python implementations: CPython, PyPy.

These mpi4py wheels are distributed via the Python Package Index (PyPI) and can be installed with Python package managers like pip:

python -m pip install mpi4py

The mpi4py wheels can be installed in standard Python virtual environments. The MPI runtime can be provided by other wheels installed in the same virtual environment.

Tip

Intel publishes production-grade Intel MPI wheels for Linux (x86_64) and Windows (AMD64). mpi4py and MPI wheels can be installed side by side to get a ready-to-use Python+MPI environment:

python -m pip install mpi4py impi-rt

Tip

The mpi4py project publishes MPICH wheels and Open MPI wheels for Linux (x86_64/aarch64) and macOS (arm64/x86_64). mpi4py and MPI wheels can be installed side by side to get a ready-to-use Python+MPI environment:

python -m pip install mpi4py mpich    # for MPICH
python -m pip install mpi4py openmpi  # for Open MPI

Warning

The MPI wheels are distributed with special focus on ease of use, convenience, compatibility, and interoperability. The Linux wheels are built in somewhat constrained environments with relatively dated Linux distributions (manylinux container images). Therefore, they may lack support for features like GPU awareness (CUDA/ROCm) and C++/Fortran bindings. In production scenarios, it is recommended to use external (either custom-built or system-provided) MPI installations.

The mpi4py wheels can also be installed (with pip) in conda environments and they should work out of the box, without any special tweak to environment variables, for any of the MPI packages provided by conda-forge.

Externally-provided MPI implementations may come from a system package manager, sysadmin-maintained builds accessible via module files, or customized user builds. Such usage is supported and encouraged. However, there are a few platform-specific considerations to take into account.

Linux

The Linux (x86_64/aarch64) wheels require one of

Users may need to set the LD_LIBRARY_PATH environment variable such that the dynamic linker is able to find at runtime the MPI shared library file (libmpi.so.*).

Fedora/RHEL

On Fedora/RHEL systems, both MPICH and Open MPI are available for installation. There is no default or preferred MPI implementation. Instead, users must select their favorite MPI implementation by loading the proper MPI module.

module load mpi/mpich-$(arch)    # for MPICH
module load mpi/openmpi-$(arch)  # for Open MPI

After loading the requested MPI module, the LD_LIBRARY_PATH environment variable should be properly setup.

Debian/Ubuntu

On Debian/Ubuntu systems, Open MPI is the default MPI implementation and most of the MPI-based applications and libraries provided by the distribution depend on Open MPI. Nonetheless, MPICH is also available to users for installation.

In Ubuntu 22.04 and older, due to legacy reasons, the MPICH ABI is slightly broken: the MPI shared library file is named libmpich.so.12 instead of libmpi.so.12 as required by the MPICH ABI Compatibility Initiative.

Users without sudo access can workaround this issue creating a symbolic link anywhere in their home directory and appending to LD_LIBRARY_PATH.

mkdir -p ~/.local/lib
libdir=/usr/lib/$(arch)-linux-gnu
ln -s $libdir/libmpich.so.12 ~/.local/lib/libmpi.so.12
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.local/lib

A system-wide fix for all users requires sudo access:

libdir=/usr/lib/$(arch)-linux-gnu
sudo ln -sr $libdir/libmpi{ch,}.so.12

HPE Cray OS

On HPE Cray systems, users must load the cray-mpich-abi module. For further details, refer to man intro_mpi.

macOS

The macOS (arm64/x86_64) wheels require

Windows

The Windows (AMD64) wheels require one of

User may need to set the I_MPI_ROOT or MSMPI_BIN environment variables such that the MPI dynamic link library (DLL) (impi.dll or msmpi.dll) can be found at runtime.

Intel MPI is under active development and supports recent versions of the MPI standard. Intel MPI can be installed with pip (see the impi-rt package on PyPI), being therefore straightforward to get it up and running within a Python environment. Intel MPI can also be installed system-wide as part of the Intel oneAPI HPC Toolkit for Windows or via standalone online/offline installers.

Conda packages

The conda-forge community provides ready-to-use binary packages from an ever growing collection of software libraries built around the multi-platform conda package manager. Four MPI implementations are available on conda-forge: Open MPI (Linux and macOS), MPICH (Linux and macOS), Intel MPI (Linux and Windows), and Microsoft MPI (Windows). You can install mpi4py and your preferred MPI implementation using the conda package manager:

  • to use MPICH do:

    conda install -c conda-forge mpi4py mpich

  • to use Open MPI do:

    conda install -c conda-forge mpi4py openmpi

  • to use Intel MPI do:

    conda install -c conda-forge mpi4py impi_rt

  • to use Microsoft MPI do:

    conda install -c conda-forge mpi4py msmpi

MPICH and many of its derivatives are ABI-compatible. You can provide the package specification mpich=X.Y.*=external_* (where X and Y are the major and minor version numbers) to request the conda package manager to use system-provided MPICH (or derivative) libraries. Similarly, you can provide the package specification openmpi=X.Y.*=external_* to use system-provided Open MPI libraries.

The openmpi package on conda-forge has built-in CUDA support, but it is disabled by default. To enable it, follow the instruction outlined during conda install. Additionally, UCX support is also available once the ucx package is installed.

Warning

The MPI conda-forge packages are built with special focus on compatibility. The MPICH and Open MPI packages are built in a constrained environment with relatively dated OS images. Therefore, they may lack support for high-performance features like cross-memory attach (XPMEM/CMA). In production scenarios, it is recommended to use external (either custom-built or system-provided) MPI installations. See the relevant conda-forge documentation about using external MPI libraries .

System packages

mpi4py is readily available through system package managers of most Linux distributions and the most popular community package managers for macOS.

Linux

On Fedora Linux systems (as well as RHEL and their derivatives using the EPEL software repository), you can install binary packages with the system package manager:

  • using dnf and the mpich package:

    sudo dnf install python3-mpi4py-mpich

  • using dnf and the openmpi package:

    sudo dnf install python3-mpi4py-openmpi

Please remember to load the correct MPI module for your chosen MPI implementation:

  • for the mpich package do:

    module load mpi/mpich-$(arch)
python -c "from mpi4py import MPI"

  • for the openmpi package do:

    module load mpi/openmpi-$(arch)
python -c "from mpi4py import MPI"

On Ubuntu Linux and Debian Linux systems, binary packages are available for installation using the system package manager:

sudo apt install python3-mpi4py

On Arch Linux systems, binary packages are available for installation using the system package manager:

sudo pacman -S python-mpi4py

macOS

macOS users can install mpi4py using the Homebrew package manager:

brew install mpi4py

Note that the Homebrew mpi4py package uses Open MPI. Alternatively, install the mpich package and next install mpi4py from sources using pip.

Alternatively, mpi4py can be installed from MacPorts:

sudo port install py-mpi4py

Building from sources

Installing mpi4py from pre-built binary wheels, conda packages, or system packages is not always desired or appropriate. For example, the mpi4py wheels published on PyPI may not be interoperable with non-mainstream, vendor-specific MPI implementations; or a system mpi4py package may be built with a alternative, non-default MPI implementation. In such scenarios, mpi4py can still be installed from its source distribution (sdist) using pip:

python -m pip install --no-binary=mpi4py mpi4py

You can also install the in-development version with:

python -m pip install git+https://github.com/mpi4py/mpi4py

or:

python -m pip install https://github.com/mpi4py/mpi4py/tarball/master

Note

Installing mpi4py from its source distribution (available on PyPI) or Git source code repository (available on GitHub) requires a C compiler and a working MPI implementation with development headers and libraries.

Warning

pip keeps previously built wheel files in its cache for future reuse. If you want to reinstall the mpi4py package from its source distribution using a different or updated MPI implementation, you have to either first remove the cached wheel file:

python -m pip cache remove mpi4py
python -m pip install --no-binary=mpi4py mpi4py

or ask pip to disable the cache:

python -m pip install --no-cache-dir --no-binary=mpi4py mpi4py

Build backends

mpi4py supports three different build backends: setuptools (default), scikit-build-core (CMake-based), and meson-python (Meson-based). The build backend can be selected by setting the MPI4PY_BUILD_BACKEND environment variable.

MPI4PY_BUILD_BACKEND
Choices:

"setuptools", "scikit-build-core", "meson-python"

Default:

"setuptools"

Request a build backend for building mpi4py from sources.

Using setuptools

Tip

Set the MPI4PY_BUILD_BACKEND environment variable to "setuptools" to use the setuptools build backend.

When using the default setuptools build backend, mpi4py relies on the legacy Python distutils framework to build C extension modules. The following environment variables affect the build configuration.

MPI4PY_BUILD_MPICC

The mpicc compiler wrapper command is searched for in the executable search path (PATH environment variable) and used to compile the mpi4py.MPI C extension module. Alternatively, use the MPI4PY_BUILD_MPICC environment variable to the full path or command corresponding to the MPI-aware C compiler.

MPI4PY_BUILD_MPILD

The mpicc compiler wrapper command is also used for linking the mpi4py.MPI C extension module. Alternatively, use the MPI4PY_BUILD_MPILD environment variable to specify the full path or command corresponding to the MPI-aware C linker.

MPI4PY_BUILD_MPICFG

If the MPI implementation does not provide a compiler wrapper, or it is not installed in a default system location, all relevant build information like include/library locations and library lists can be provided in an ini-style configuration file under a [mpi] section. mpi4py can then be asked to use the custom build information by setting the MPI4PY_BUILD_MPICFG environment variable to the full path of the configuration file. As an example, see the mpi.cfg file located in the top level mpi4py source directory.

MPI4PY_BUILD_CONFIGURE

Some vendor MPI implementations may not provide complete coverage of the MPI standard, or may provide partial features of newer MPI standard versions while advertising support for an older version. Setting the MPI4PY_BUILD_CONFIGURE environment variable to a non-empty string will trigger the run of exhaustive checks for the availability of all MPI constants, predefined handles, and routines.

MPI4PY_BUILD_MPIABI

Setting the MPI4PY_BUILD_MPIABI environment variable to "1" enables enhanced support for the MPI 5.0 standard ABI and the MPICH or Open MPI legacy ABIs. The mpi4py.MPI extension module is then able to dynamically link at runtime with older versions (down to the MPI 3.0 standard) of the corresponding MPI implementation used at compile time. This feature is only available on Linux, macOS, and Windows. POSIX-like systems other than Linux and macOS are not currently supported, although they could easily be: all what is needed is for the platform to either support weak symbols in shared modules/libraries, or support the standard POSIX dlopen()/dlsym() APIs.

MPI4PY_BUILD_PYSABI

Build with the CPython 3.10+ Stable ABI. Setting the MPI4PY_BUILD_PYSABI environment variable to a string "{major}.{minor}" defines the Py_LIMITED_API value to use for building extension modules.

The following environment variables are aliases for the ones described above. Having shorter names, they are convenient for occasional use in the command line. Its usage is not recommended in automation scenarios like packaging recipes, deployment scripts, and container image creation.

MPICC

Convenience alias for MPI4PY_BUILD_MPICC.

MPILD

Convenience alias for MPI4PY_BUILD_MPILD.

MPICFG

Convenience alias for MPI4PY_BUILD_MPICFG.

Using scikit-build-core

Tip

Set the MPI4PY_BUILD_BACKEND environment variable to "scikit-build-core" to use the scikit-build-core build backend.

When using the scikit-build-core build backend, mpi4py delegates all of MPI build configuration to CMake’s FindMPI module. Besides the obvious advantage of cross-platform support, this delegation to CMake may be convenient in build environments exposing vendor software stacks via intricate module systems. Note however that mpi4py will not be able to look for MPI routines available beyond the MPI standard version the MPI implementation advertises to support (via the MPI_VERSION and MPI_SUBVERSION macro constants in the mpi.h header file), any missing MPI constant or symbol will prevent a successful build.

Using meson-python

Tip

Set the MPI4PY_BUILD_BACKEND environment variable to "meson-python" to use the meson-python build backend.

When using the meson-python build backend, mpi4py delegates build tasks to the Meson build system.

Warning

mpi4py support for the meson-python build backend is experimental. For the time being, users must set the CC environment variable to the command or path corresponding to the mpicc C compiler wrapper.