Using IPython for parallel computing#

Release:

9.1.0.dev

Date:

Dec 09, 2024

Installing IPython Parallel#

As of 4.0, IPython parallel is now a standalone package called ipyparallel. You can install it with:

pip install ipyparallel

or:

conda install ipyparallel

As of IPython Parallel 7, this will include installing/enabling an extension for both the classic Jupyter Notebook and JupyterLab ≥ 3.0.

Quickstart#

IPython Parallel

A quick example to:

allocate a cluster (collection of IPython engines for use in parallel)
run a collection of tasks on the cluster
wait interactively for results
cleanup resources after the task is done

import time
import ipyparallel as ipp

task_durations = [1] * 25
# request a cluster
with ipp.Cluster() as rc:
    # get a view on the cluster
    view = rc.load_balanced_view()
    # submit the tasks
    asyncresult = view.map_async(time.sleep, task_durations)
    # wait interactively for results
    asyncresult.wait_interactive()
    # retrieve actual results
    result = asyncresult.get()
# at this point, the cluster processes have been shutdown

You can similarly run MPI code using IPyParallel (requires mpi4py):

import ipyparallel as ipp

def mpi_example():
    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    return f"Hello World from rank {comm.Get_rank()}. total ranks={comm.Get_size()}"

# request an MPI cluster with 4 engines
with ipp.Cluster(engines='mpi', n=4) as rc:
    # get a broadcast_view on the cluster which is best
    # suited for MPI style computation
    view = rc.broadcast_view()
    # run the mpi_example function on all engines in parallel
    r = view.apply_sync(mpi_example)
    # Retrieve and print the result from the engines
    print("\n".join(r))
# at this point, the cluster processes have been shutdown