Concurrent and Parallel Programming in Python

Concurrent and Parallel Programming in Python

Instructor: Packt - Course Instructors

What you'll learn

Understand the fundamental concepts of concurrent and parallel programming in Python.
Implement threading, multiprocessing, and asynchronous programming techniques.
Design multi-process programs that execute faster and more efficiently.
Apply techniques to distribute workloads across all available CPU cores on a machine.

Details to know

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Assessments

1 assignment

Taught in English

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There are 3 modules in this course

Dive into the world of concurrent and parallel programming with this detailed course designed for Python developers. Begin with threading, learning how to create and manage threads, and progress to building complex systems with threading classes. Explore practical projects like creating a Wikipedia reader and a Yahoo Finance reader, mastering the use of queues, schedulers, and database integration.

Advance to multiprocessing, understanding how to leverage multiple CPU cores for enhanced performance. Learn to implement multiprocessing queues and pools, handle multiple arguments, and optimize CPU-intensive tasks. Finally, delve into asynchronous programming, mastering async tasks, timeouts, and libraries. Combine asynchronous and multiprocessing techniques for robust and scalable applications. This course provides a thorough understanding of concurrent and parallel programming, preparing you to tackle real-world challenges and optimize your Python applications for performance and efficiency. This course is ideal for Python developers, software engineers, and data scientists who want to enhance their skills in concurrent and parallel programming. A basic understanding of Python is recommended.

In this module, we will delve into the fundamentals of threading in Python. We'll start with an introduction to threading, multiprocessing, and asynchronous programming concepts. Then, we will explore the creation and management of threads, develop practical applications like a Wikipedia reader and Yahoo Finance reader, and integrate advanced features such as queues, schedulers, and locking mechanisms to optimize our concurrent and parallel programming skills.

What's included

14 videos1 reading

14 videosTotal 261 minutes

Threading, Multiprocessing, Async Introduction13 minutesPreview module
Threading in Python18 minutes
Creating a Threading Class14 minutes
Creating a Wikipedia Reader14 minutes
Creating a Yahoo Finance Reader16 minutes
Queues and Master Scheduler15 minutes
Creating a Postgres Worker21 minutes
Integrating the Postgres Worker20 minutes
Yaml File Introduction18 minutes
Creating a Yaml Reader30 minutes
Improving Our Wiki Worker28 minutes
Improving All Workers and Adding Monitoring29 minutes
Final Program Cleanup7 minutes
Locking12 minutes

1 readingTotal 10 minutes

Full Course Resources10 minutes

In this module, we will explore the powerful capabilities of multiprocessing in Python. We'll begin with an introduction to the core concepts and advantages of using multiprocessing. Next, we will dive into practical implementations, including the use of multiprocessing queues, pools, and advanced techniques for handling multiple arguments and tasks. Finally, we will cover methods for efficiently processing and checking elements in lists within specific ranges, ensuring optimal performance in concurrent and parallel programming tasks.

What's included

6 videos

In this module, we will cover the essentials of asynchronous programming in Python. We'll start with an introduction to writing asynchronous programs and managing asynchronous tasks. You'll learn to use the async gather method and implement timeouts for handling multiple asynchronous operations effectively. We will also explore creating asynchronous for loops and using various asynchronous libraries. Finally, we'll delve into combining asynchronous programming with multiprocessing to achieve the highest level of concurrency and performance in your Python applications.