Complete Guide to Python Multithreading and Multiprocessing from Udemy

What's inside

Learning objectives

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.

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.

Syllabus

Set up their Python environment

Introduction to Python paralleling

Installing Python and Setting Up Your Environment

How to Install Python 3 and Use Virtual Environments (venv) on Windows- Article

How to Install Python 3 and Use Virtual Environments (venv) on linux- Article

How to Install Python 3 and Use Virtual Environments (venv) on Mac- Article

Installing Pycharm - Article

What will students be able to define and use functions, understand object-oriented programming, organize code with modules, and enhance functions with decorators.

Functions in Python: Definition and Usage

Functions in Python: Definition and Usage - QUIZ

Functions in Python: Definition and Usage - Code Exercise

Modules and Packages: Organizing Code

Modules and Packages: Organizing Code - Coding Exercise

Modules and Packages: Organizing Code - QUIZ

Understanding Python Classes and Objects

Understanding Python Classes and Objects - QUIZ

Understanding Python Classes and Objects - Coding Exercise

be able to distinguish between concurrency and parallelism and understand the role of the GIL in Python threading.

Understanding Concurrency: Threads vs. Processes

Threads in Python and the Global Interpreter Lock (GIL)

students be able to create, manage, and synchronize threads in Python applications.

Creating Your First Thread - Presentation

Creating Your First Thread - Code

Joining Threads: Understanding join() - Presentation

Joining Threads: Understanding join() - Code

Joining Threads - Code Exercise

Daemon Threads: Background Execution - Presentation

Daemon Threads: Background Execution - Code

Daemon Threads - Code Exercise

Basics of Threading in Python Quiz

students be able to implement advanced threading concepts such as locks, deadlocks, and condition variables for efficient thread synchronization.

Thread Synchronization: Using Locks - Presentation

Thread Synchronization: Using Locks - Code

Thread Synchronization: Using Locks Quiz

Deadlocks: Identification and Prevention - Presentation

Deadlocks: Identification and Prevention - Code

Deadlocks: Identification and Prevention - Quiz

Condition Variables and Wait Groups - Presentation

Condition Variables and Wait Groups - Code

Condition Variables and Wait Groups Quiz

Condition Variables and Wait Groups - Code Exercise

Using Barriers in Threading - Presentation

Using Barriers in Threading - Code

Using Barriers in Threading - Code Exercise

Using Barriers in Threading - Quiz

students be able to utilize multiprocessing to create and manage processes for executing tasks in parallel.

Understanding Processes in Python - Presentation

Understanding Processes in Python - Code

Understanding Processes in Python - Code Exercise

Creating and Managing Processes - Presentation

Creating and Managing Processes - Code

Creating and Managing Processes - Quiz

Memory Sharing Between Processes - Presentation

Memory Sharing Between Processes: Value - Code

Memory Sharing Between Processes: Array - Code

Memory Sharing Between Processes - Quiz

Communication Between Processes: Pipes and Queues - Presentation

Communication Between Processes: Pipes and Queues - Code - Part 1

Communication Between Processes: Pipes and Queues - Code - Part 2

Communication Between Processes: Pipes and Queues - Quiz

students be able to apply threading and multiprocessing concepts to real-world problems, enhancing application performance.

File Searching with Threads and Processes - Presentation

File Searching with Threads and Processes - Code

Matrix Multiplication: Comparing Single and Multi-threaded - Presentation

students be able to efficiently manage multiple threads and processes using pooling techniques.

Using ThreadPoolExecutor and ProcessPoolExecutor - Presentation

Using ThreadPoolExecutor and ProcessPoolExecutor - Code

Using ThreadPoolExecutor and ProcessPoolExecutor - Quiz

Real-world Applications of Pools - Presentation

Real-world Applications of Pools - Code

Real-world Applications of Pools - Quiz

Debugging Multithreaded and Multiprocess Applications - Presentation

Debugging Multithreaded and Multiprocess Applications - Code

Debugging Multithreaded and Multiprocess Applications - Quiz

Extra Content - Creating Logger class using logging library - Code

students be able to review key concepts and identify paths for further exploration and learning in concurrent programming.

Review and Key Takeaways

Additional Resources for Advanced Learning

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops valuable industry skills in parallel programming, boosting career prospects

Taught by experienced developer Idan Chen, recognized expert in concurrent programming

Comprehensive coverage of both fundamental and advanced concepts, building a solid foundation

Leverages hands-on exercises and real-world examples, enhancing practical application

In-depth exploration of the GIL in Python, providing a clear understanding of its impact on threading

Requires some prior knowledge in Python programming, may not be suitable for complete beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Guide to Python Multithreading and Multiprocessing with these activities:

Review Python basics

Show steps

Prepare for this course by reviewing Python basics to ensure you have a strong knowledge of its syntax and structures.

Show steps

Review functions in Python
Review classes in Python
Review modules in Python
Practice writing simple Python programs

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Career center

Learners who complete Complete Guide to Python Multithreading and Multiprocessing will develop knowledge and skills that may be useful to these careers:

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Complete Guide to Python Multithreading and Multiprocessing

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