Introduction to IPython parallel

The following demos heavily rely on IPython-parallel to illustrate how checkpointing works when using multiple MPI processes. We illustrate what happens in parallel by launching three MPI processes using ipyparallel

import logging

import ipyparallel as ipp

def hello_mpi():
    # We define all imports inside the function as they have to be launched on the remote engines
    from mpi4py import MPI

    print(f"Hello from rank {MPI.COMM_WORLD.rank}/{MPI.COMM_WORLD.size - 1}")

with ipp.Cluster(engines="mpi", n=3, log_level=logging.ERROR) as cluster:
    # We send the query to run the function `hello_mpi` on all engines
    query = cluster[:].apply_async(hello_mpi)
    # We wait for all engines to finish
    query.wait()
    # We check that all engines exited successfully
    assert query.successful(), query.error
    # We print the output from each engine
    print("".join(query.stdout))

Hello from rank 0/2
Hello from rank 1/2
Hello from rank 2/2