This is the code repository for Concurrent and Distributed Computing with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- Create and manage threads, and overcome the infamous Python GIL (Global Interpreter Lock)
- Synchronize your processes and create process pools
- Implement concurrent futures module through asynchronous programming
- Practice AsyncResult and Celery backends through examples
- See how you can implement concurrency using Celery
- Execute client-server applications using Pyro as an alternative to Celery
- Create a Python SQS distributed background worker
To fully benefit from the coverage included in this course, you will need:
This course has the following software requirements:
- Operating system: Windows, Mac or Linux
- Browser: Any modern browser
- PyCharm or any other code editor like Atom, Sublime, etc.
- AWS account(Free tier or above)
- Python 3.x