Complete Guide To Python Multithreading And Multiprocessing
Free Download 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.
Description
Unlock the Power of Concurrent Programming with PythonWelcome to "The Complete Guide to Python Multithreading and Multiprocessing," your comprehensive journey into the world of parallel programming in Python. Whether you're looking to boost the performance of your applications or simply curious about how concurrent programming works, this course is designed to equip you with the skills and knowledge you need to master threading and processing in Python.What You Will LearnThroughout this course, we will delve deep into the essentials and advanced concepts of multithreading and multiprocessing in Python. Starting with the basics, you'll first get acquainted with Python's programming environment and fundamental concepts. As we progress, you'll:Understand the difference between concurrency and parallelism, and when to use each.Explore the threading module to create, manage, and synchronize threads efficiently.Dive into Python's multiprocessing module to execute processes in parallel, enhancing your application's performance.Learn about process communication and memory sharing between processes, crucial for complex parallel applications.Apply your knowledge through practical, real-world examples, from file searching to web scraping and matrix multiplication.Tackle advanced topics such as thread and process pooling, deadlock identification, prevention, and debugging techniques for concurrent programming.Who Is This Course For?This course is designed for:Programmers and developers with a basic understanding of Python looking to enhance their skills.Software engineers are interested in making their applications faster and more efficient through parallel programming.Anyone curious about how concurrency works in Python and eager to apply these concepts to real-world problems.Why Choose This Course?Comprehensive Curriculum: From the foundational concepts to advanced techniques, this course covers everything you need to know about multithreading and multiprocessing in Python.Practical Application: Learn through doing hands-on exercises and real-world examples that ensure you can apply what you've learned immediately.Expert Instruction: Benefit from detailed explanations and tips from an experienced developer who's passionate about making concurrency accessible to everyone.Join us on this journey through the powerful world of concurrent programming in Python, and unlock the potential to create faster, more efficient, and scalable applications. Enroll in "The Complete Guide to Python Multithreading and Multiprocessing" today and take the first step towards mastering Python parallel programming.Idan Chen
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 20 Deadlocks: Identification and Prevention - Presentation
Lecture 21 Deadlocks: Identification and Prevention - 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.
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