Running Parallel Jobs on JupyterHub with ipyparallel

JupyterHub offers a multi user environment for running Jupyter Notebooks. Research Computing provides access to a JupyterHub environment with parallel processing support. This tutorial will demonstrate how to use the ipyparallel python package to run simple parallel jobs on JupyterHub.


  • Connect to a remote cluster for parallel processing
  • Use ipyparallel to run parallel jobs


  • ipyparallel

Using ipyparallel

Begin by installing the ipyparallel package

!pip install ipyparallel

You will need to specify the cluster you will be running on, and connect to that cluster. For this example, we will be starting a client with 4 engines using the $ ipcluster command. Information on the clusters available through JupyterHub can be found here. The

!ipcluster start -n 4
2016-07-08 11:46:24.158 [IPClusterStart] CRITICAL | Cluster is already running with [pid=7580]. use "ipcluster stop" to stop the cluster.
from ipyparallel import Client
cluster = Client()
c = cluster[:]

Now that you have created a connection to a cluster, you can start to run simple parallel jobs. The following code will calculate the square of each number from 1-28 and print the results.

squares = c.map_sync(lambda x: x**2, range(1,28))
squares: [1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729]

There are several methods for running functions in parallel using ipyparallel. The easiest way to do this is using the apply_sync() method within the ipyparallel class. The following function will take two matricies and return the result of their multiplaction. By using the apply_sync() function with the name of the function to be executed as the first argument, and the functions arguments as the remaining arguments, the matrix multiplication will be performed in parallel on the connected cluster. This can be done with any python functions, including built in functions.

import numpy as np
y = np.random.randn(50,50)
x = np.matrix(y)
def matmul(x, y):
    return x*y
res = c.apply_sync(matmul, x, x)

If you are creating a function with element-wise operations (perorm one opperation on every element of an array), you can use the @parallel tag to break the function up and process it in parallel.

def arrmul(x,y):
    return x*y
res = arrmul(y, y)

When you have finished using the clusters, it is important to free the resources you have been using. For this example, this can be done using the $ ipcluster command again. If you are running your job in JupyterHub you can find more information on the rescources available, and how to use them here.

!ipcluster stop
2016-07-08 11:46:32.410 [IPClusterStop] Removing pid file: C:\Users\Zach\.ipython\profile_default\pid\


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