October 2015
Beginner
200 pages
3h 56m
English
book
Learning IPython for Interactive Computing and Data Visualization, Second Edition
by Cyrille Rossant
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Distributing tasks on several cores with IPython.parallel
In the previous sections, we covered a few methods to accelerate Python code. Here, we will see how to run multiple tasks in parallel on a multicore computer. IPython implements highly-powerful and user-friendly facilities for interactive parallel computing in the Notebook.
We first need to install ipyparallel (also called IPython.parallel) with conda install ipyparallel. Next, let's import NumPy and ipyparallel:
In [1]: import numpy as np # ipyparallel was IPython.parallel before IPython 4.0 from ipyparallel import Client
To use IPython.parallel, we need to launch a few engines.
The first way to do it is to run ipcluster start in the terminal.
You can also launch engines from the Notebook ...
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