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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

Debugging


Everything is great when things work as we expect them to; oftentimes, however, we are not so lucky. Distributed applications, and even simple jobs running remotely, are particularly challenging to debug. It is usually hard to know exactly which user account our jobs run under, which environment they are executed in, where they run, and, with job schedulers, it is even hard to predict when they will run.

When things do not work as we expect them to, there are a few places where we could get some hints as to what happened. When working with a job scheduler, the first thing to do is look at any error messages returned by the job submission tool (that is, condor_submit, condor_submit_dag, or qsub). The second place to look for clues are the job STDOUT, STDERR, and log files.

Usually, the job scheduler itself has tools to diagnose problematic jobs. HTCondor, for instance, provides condor_q -better-analyze to investigate why a given job might be stuck in the queue longer than expected...

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