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# # Introduction to IPython parallel
# The following demos heavily rely on {py:mod}`IPython-parallel<ipyparallel>` to illustrate how
# checkpointing works when using multiple {py:mod}`MPI<mpi4py>` processes.
# We illustrate what happens in parallel by launching three MPI processes
# using IPython-parallel's MPI engine support.
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: ipp.AsyncResult = 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))
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