Installation

Basic installation

Start by installing a suitable version of JAX and jaxlib. If you don’t plan on using mpi4jax on GPU, the following will do:

$ pip install jax jaxlib

Note

Much of the functionality we need has recently been added to JAX, which itself is changing frequently. Therefore, mpi4jax has somewhat strict requirements on the supported versions of JAX and jaxlib. Be prepared to upgrade!

We recommend that you use pip to install mpi4jax (but a distribution is also available via conda which will work if MPI, mpi4py and mpi4jax are all installed through conda ):

$ pip install mpi4jax
$ conda install -c conda-forge mpi4jax

Installing via pip requires a working installation of MPI to succeed. If you don’t already have MPI and want to get started as quickly as possible, try conda, which bundles the MPI library (but remember not to mix pip and conda).

Warning

We advise against using the conda installation in HPC environments because it is not possible to change the MPI library mpi4py is linked against.

And that is it! If you are familiar with MPI, you should in principle be able to get started right away. However, we recommend that you have a look at 🔪 The Sharp Bits 🔪, to make sure that you are aware of some of the pitfalls of mpi4jax.

Selecting the MPI distribution

mpi4jax will use the MPI distribution with which mpi4py was built. If mpi4py is not installed, it will be installed automatically before installing mpi4jax.

To check which MPI library both libraries link to, run the following command in your prompt.

$ python -c "import mpi4py; print(mpi4py.get_config())"

If you wish to use a specific MPI library (only possible when using pip), it is usually sufficient to specify the MPICC environment variable before installing `mpi4py.

See also

In doubt, please refer to the mpi4py documentation.

Installation with GPU support

Note

To use JAX on the GPU, make sure that your jaxlib is built with CUDA support.

mpi4jax also supports JAX arrays stored in GPU memory.

To build mpi4jax’s GPU extensions, we need to be able to locate the CUDA headers on your system. If they are not detected automatically, you can set the environment variable CUDA_ROOT when installing mpi4jax:

$ CUDA_ROOT=/usr/local/cuda pip install mpi4jax

This is sufficient for most situations. However, mpi4jax will copy all data from GPU to CPU and back before and after invoking MPI.

If this is a bottleneck in your application, you can build MPI with CUDA support and communicate directly from GPU memory. This requires that you re-build the entire stack:

  • Your MPI library, e.g. OpenMPI, with CUDA support.

  • mpi4py, linked to your CUDA-enabled MPI installation.

  • mpi4jax, using the correct mpi4py installation.

See also

Read here on how to use zero-copy GPU communication after installation.