Using IPython for parallel computing

Release

7.0.1

Date

Sep 16, 2021

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:

  1. allocate a cluster (collection of IPython engines for use in parallel)

  2. run a collection of tasks on the cluster

  3. wait interactively for results

  4. 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

Follow the tutorial to learn more.

Indices and tables