Going forward we discover how you can use Python’s multiprocessing library to run functions in parallel. Threads and processes often need to share or exchange data. In asynchronous code just passing Python objects is usually not the safest way to do this. This course looks at the main ways to do this correctly in Python, such as queues and events. Finally, the course moves on to the concurrent.future library which contains higher-level abstractions, including thread and processing pools and an asynchronous map function. The course finishes with advice on how to write robust asynchronous code, and how to test and debug it.

What You Will Learn

Take advantage of multi threading (concurrency) using the threading module Implement multi processing (parallelism) using the multiprocessing module Understand inter-process communication and data sharing using locks, queues, semaphores, barriers, events, and timers Test and debug asynchronous Python code Understand thread and process pools using concurrent.futures Who This Video Is For Intermediate to experienced Python programmers who want to speed up existing Python code by using multi processing and multi threading.