Complete Guide To Python Multithreading And Multiprocessing

Complete Guide To Python Multithreading And Multiprocessing
Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 812.16 MB | Duration: 3h 12m

Parallel Programming in Python: Mastering Threading & Processing for Efficient Concurrency

What you'll learn
Fundamentals of Python Programming: Refresh your knowledge on Python basics including functions, classes, modules, and decorators.
Concurrency vs. Parallelism: Understand the difference and when to use each in your applications.
Introduction to Threading in Python: Learn how to create, manage, and synchronize threads using the threading module.
Deep Dive into the Global Interpreter Lock (GIL): Discover how the GIL affects multithreading in Python and strategies to work around it.
Basics of Multiprocessing: Utilize the multiprocessing module to run code across multiple CPU cores for true parallelism.
Process Communication: Master techniques for sharing data and messages between processes to build efficient parallel applications.
Advanced Threading and Multiprocessing Techniques: Explore thread pools, process pools, locks, semaphores, and condition variables to handle complex concurrency
Debugging and Optimizing: Learn how to debug and optimize threaded and multiprocessed applications for maximum performance.
Best Practices: Gain insights into best practices for writing clean, efficient, and scalable concurrent code.

Requirements
Basic Python Knowledge: Familiarity with Python syntax and basic programming constructs such as variables, loops, and functions.
Understanding of Python Data Structures: Comfortable working with lists, dictionaries, sets, and tuples.
Introductory Level of Object-Oriented Programming: Basic understanding of classes and objects in Python.
Software Requirements: Python installed on your computer (version 3.x recommended) along with an integrated development environment (IDE) or text editor of your choice.
A Willingness to Learn: An open mind and readiness to dive into the more complex topics of multithreading and multiprocessing.

Code:

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