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ipyparallel

 * Tutorial
 * Reference
 * examples
 * Changelog
 * API Reference

 * GitHub

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On this page
 * Using IPython for parallel computing
   * Installing IPython Parallel
   * Quickstart
   * Contents
 * IPython Parallel API
 * Indices and tables




USING IPYTHON FOR PARALLEL COMPUTING#

> Release
> 
> 8.4.0.dev
> 
> Date
> 
> 2022-05-18


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


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




Follow the tutorial to learn more.


CONTENTS#

 * Tutorial
   * Overview and getting started
   * Starting the IPython controller and engines
   * IPython’s Direct interface
   * Parallel Magic Commands
   * The IPython task interface
   * The AsyncResult object
   * Parallel examples
 * Reference
   * Using MPI with IPython
   * IPython’s Task Database
   * Security details of IPython Parallel
   * DAG Dependencies
   * Details of Parallel Computing with IPython
   * Messaging for Parallel Computing
   * Connection Diagrams of The IPython ZMQ Cluster
   * Launchers

 * examples
   * Tutorials
   * Examples
   * Integrating IPython Parallel with other tools
   * Non-notebook examples

 * Changelog


IPYTHON PARALLEL API#

 * API Reference
   * Classes
   * Decorators
   * Exceptions


INDICES AND TABLES#

 * Index

 * Module Index

 * Search Page

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Tutorial

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