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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

Installing Celery

Celery (http://www.celeryproject.org) is the first third-party library that we encounter in this book, since so far, we have only looked at modules and packages in the Python standard library. Celery is a distributed task queue, meaning that it is a queue-based system like some of the ones that we built in the previous chapters. It is also distributed, which means that worker processes, as well as the queues holding results and work requests, typically run on different machines.

Let's start by installing Celery and its dependencies. We start by setting up a virtual environment on each machine (let's call it book so that we know it is related to the examples in this book), as shown in the following line of code (assuming a Unix environment):

$ pip install virtualenvwrapper

If the preceding command fails with a permission denied error, then you can use sudo to install virtualenvwrapper as a super-user, as shown in the following command:

$ sudo pip install virtualenvwrapper...
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