Chapter 5. Distributed Python
In this chapter, we will cover the following recipes:
- Using Celery to distribute tasks
- How to create a task with Celery
- Scientific computing with SCOOP
- Handling map functions with SCOOP
- Remote method invocation with Pyro4
- Chaining objects with Pyro4
- Developing a client-server application with Pyro4
- Communicating sequential processes with PyCSP
- Using MapReduce with Disco
- A remote procedure call with RPyC
Introduction
The basic idea of distributed computing is to break each workload into an arbitrary number of tasks, usually indicated with the name, into reasonable pieces for which a computer in a distributed network will be able to finish and return the results flawlessly. In distributed computing, there is the absolute certainty ...
Get Python Parallel Programming Cookbook now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
