Book Image

Mastering IPython 4.0

By : Thomas Bitterman, Dipanjan Deb
Book Image

Mastering IPython 4.0

By: Thomas Bitterman, Dipanjan Deb

Overview of this book

IPython is an interactive computational environment in which you can combine code execution, rich text, mathematics, plots, and rich media. This book will get IPython developers up to date with the latest advancements in IPython and dive deep into interactive computing with IPython. This an advanced guide on interactive and parallel computing with IPython will explore advanced visualizations and high-performance computing with IPython in detail. You will quickly brush up your knowledge of IPython kernels and wrapper kernels, then we'?ll move to advanced concepts such as testing, Sphinx, JS events, interactive work, and the ZMQ cluster. The book will cover topics such as IPython Console Lexer, advanced configuration, and third-party tools. By the end of this book, you will be able to use IPython for interactive and parallel computing in a high-performance computing environment.
Table of Contents (18 chapters)
Mastering IPython 4.0
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
6
Works Well with Others – IPython and Third-Party Tools
Index

The AsyncResult class


In our previous discussion on IPython in parallel computing (refer to Chapter 3, Stepping Up to IPython for Parallel Computing), it was demonstrated how using the map and apply functions can enable parallel computation with a minimal setup by the programmer. These methods return objects of the AsyncResult class (or a subclass). At that time, only a small subset of the class's functionality was required, so a more thorough exploration was delayed. In this section, we will provide a more complete description of the capabilities of an AsyncResult object.

The AsyncResult class provides a superset of the multiprocessing.pool.AsyncResult interface. We will start by looking at the multiprocessing.pool version before examining the new features.

multiprocessing.pool.Pool

This class allows you to create a pool of processes. The constructor takes several optional arguments, including the ones given here:

Argument

Effect

processes

This is the number of worker processes to use....