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Description of  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. Description Overview Section 1: Getting Started with Python Lecture 1 Introduction to Python paralleling Lecture 2 Installing Python and Setting Up Your Environment Lecture 3 How to Install Python 3 and Use Virtual Environments (venv) on Windows- Article Lecture 4 How to Install Python 3 and Use Virtual Environments (venv) on linux- Article Lecture 5 How to Install Python 3 and Use Virtual Environments (venv) on Mac- Article Lecture 6 Installing Pycharm - Article Section 2: Python Programming Foundations Lecture 7 Functions in Python: Definition and Usage Lecture 8 Modules and Packages: Organizing Code Lecture 9 Understanding Python Classes and Objects Section 3: Introduction to Concurrency in Python Lecture 10 Understanding Concurrency: Threads vs. Processes Lecture 11 Threads in Python and the Global Interpreter Lock (GIL) Section 4: Basics of Threading in Python Lecture 12 Creating Your First Thread - Presentation Lecture 13 Creating Your First Thread - Code Lecture 14 Joining Threads: Understanding join() - Presentation Lecture 15 Joining Threads: Understanding join() - Code Lecture 16 Daemon Threads: Background Execution - Presentation Lecture 17 Daemon Threads: Background Execution - Code Section 5: Advanced Threading Techniques Lecture 18 Thread Synchronization: Using Locks - Presentation Lecture 19 Thread Synchronization: Using Locks - Code Lecture 22 Condition Variables and Wait Groups - Presentation Lecture 23 Condition Variables and Wait Groups - Code Lecture 24 Using Barriers in Threading - Presentation Lecture 25 Using Barriers in Threading - Code Section 6: Introduction to Multiprocessing in Python Lecture 26 Understanding Processes in Python - Presentation Lecture 27 Understanding Processes in Python - Code Lecture 28 Creating and Managing Processes - Presentation Lecture 29 Creating and Managing Processes - Code Lecture 30 Memory Sharing Between Processes - Presentation Lecture 31 Memory Sharing Between Processes: Value - Code Lecture 32 Memory Sharing Between Processes: Array - Code Lecture 33 Communication Between Processes: Pipes and Queues - Presentation Lecture 34 Communication Between Processes: Pipes and Queues - Code - Part 1 Lecture 35 Communication Between Processes: Pipes and Queues - Code - Part 2 Section 7: Practical Applications of Threading and Multiprocessing Lecture 36 File Searching with Threads and Processes - Presentation Lecture 37 File Searching with Threads and Processes - Code Lecture 38 Matrix Multiplication: Comparing Single and Multi-threaded - Presentation Section 8: Pooling Techniques in Python Lecture 39 Using ThreadPoolExecutor and ProcessPoolExecutor - Presentation Lecture 40 Using ThreadPoolExecutor and ProcessPoolExecutor - Code Lecture 41 Real-world Applications of Pools - Presentation Lecture 42 Real-world Applications of Pools - Code Lecture 43 Debugging Multithreaded and Multiprocess Applications - Presentation Lecture 44 Debugging Multithreaded and Multiprocess Applications - Code Lecture 45 Extra Content - Creating Logger class using logging library - Code Section 9: Course Conclusion and Next Steps Lecture 46 Review and Key Takeaways Lecture 47 Additional Resources for Advanced Learning Intermediate Python Developers: Programmers with a solid foundation in Python looking to enhance their skills in concurrency for improved application performance.,Software Engineers: Professionals seeking to understand and implement parallel processing and threading in their Python projects.,Data Scientists and Analysts: Individuals aiming to speed up data processing tasks through parallel computing techniques.,Tech Enthusiasts: Anyone curious about how concurrent programming works in Python and eager to apply these concepts in practical scenarios.,System Administrators and DevOps Professionals: Those looking to automate and optimize tasks using Python's concurrency features for better system management.,Students and Academics: Learners in computer science or related fields seeking to deepen their understanding of concurrent programming principles and practices.