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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

Common problems – software environments


Another common challenge is making sure that the software installed on all the various machines we are ever going to use is consistent and consistently upgraded.

Unfortunately, it is frustratingly common to spend hours debugging a distributed application only to discover that for some unknown and seemingly impossible reason, some computers had an old version of the code and/or its dependencies. Sometimes, we might even find the code to have disappeared completely.

The reasons for these discrepancies can be many: from a mount point that failed, to a bug in our deployment procedures, to a simple human mistake.

A common approach, especially in the HPC world, is to always create a self-contained environment for our code before launching the application itself. Some projects go as far as preferring static linking of all dependencies to avoid having the runtime pick up the wrong version of a dynamic library.

This approach works well if the application runtime...

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