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Concurrent and Distributed Computing with Python

Video Description

Implement concurrency in your apps using Celery, and Pyro, and make your Python apps distributed with AWS

About This Video

  • See what concurrent and distributed computing can do for your code
  • Build efficient multi-task and multi-process frameworks for your apps
  • Implement threading, multiprocessing, and asynchronous programming in your apps

In Detail

Facing difficulty in implementing concurrent and multithreaded programs in your Python applications? Is this preventing you from implementing efficient code in your apps and benefiting from multiprocessing?

This course will help you resolve these difficulties. You will start by exploring the basic concepts of concurrency and distributed computing, and you'll learn which Python libraries are relevant to these. You will not only learn to see Celery as a way to build-in concurrency into your apps, but also Pyro as an alternative to Celery. You will create processes and manage processes along with interprocess communication; combine coroutines with threads and processes; practice the management of process pools; implement asynchronous tasks/job queues using AsyncResult and Celery backends; invoke remote methods in your Python-based code, and use these skills and concepts when working with AWS for Python.

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Concurrent-and-Distributed-Computing-with-Python

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.