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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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Toc

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

Multiprocess queues

When using multiple processes, the issue that comes up is how to exchange data between the workers. The multiprocessing module offers a mechanism to do that in the form of queues and pipes. Hence, we are going to look at multiprocess queues.

The multiprocessing.Queue class is modeled after the queue.Queue class with the additional twist that items stored in the multiprocessing queue need to be pickable. To illustrate how to use these queues, create a new Python script (queues.py) with the following code:

import multiprocessing as mp


def fib(n):
    if n <= 2:
        return 1
    elif n == 0:
        return 0
    elif n < 0:
        raise Exception('fib(n) is undefined for n < 0')
    return fib(n - 1) + fib(n - 2)


def worker(inq, outq):
    while True:
        data = inq.get()
        if data is None:
            return
        fn, arg = data
        outq.put(fn(arg))


if __name__ == '__main__':
    import argparse

    parser = argparse...
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