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Distributed Computing with Python

You're reading from   Distributed Computing with Python Harness the power of multiple computers using Python through this fast-paced informative guide

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781785889691
Length 170 pages
Edition 1st Edition
Languages
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Author (1):
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Rasheedh B Rasheedh B
Author Profile Icon Rasheedh B
Rasheedh B
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Table of Contents (10) Chapters Close

Preface 1. An Introduction to Parallel and Distributed Computing 2. Asynchronous Programming FREE CHAPTER 3. Parallelism in Python 4. Distributed Applications – with Celery 5. Python in the Cloud 6. Python on an HPC Cluster 7. Testing and Debugging Distributed Applications 8. The Road Ahead Index

Establishing a multimachine environment

The first thing to do before we dive into Celery and the other Python packages that we will explore is set up a test environment. We are developing distributed applications, which means that we ideally need a multimachine environment.

Those of you who have access to at least two machines in a properly set up network environment (meaning that these test machines have DNS-resolvable names) can skip to the next section. All the rest, please keep reading.

For those without easy access to multiple machines for development and testing, there are still a number of solutions that are easy to implement and either free or very inexpensive.

One is to simply use virtual machines running on the local host (for instance, using VirtualBox: https://www.virtualbox.org). Just create a couple of VMs, install your favorite Linux distribution on it, and keep them running in the background. Since these do not need a graphical desktop for development purposes, they can be...

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