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Learning Concurrency in Python

Video Description

More than 20 videos to help you master concurrency in Python

About This Video

  • Build highly efficient, robust, and concurrent applications
  • Work through practical examples that will help you address the challenges of writing concurrent code
  • Improve the overall speed of execution in multiprocessor and multicore systems and keep them highly available

In Detail

Python is a very high-level, general-purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general-purpose programming for programmers around the world. It features a wide number of powerful, high- and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to be creative.

This course introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python.

The course will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the video course, you'll have learned techniques to write incredibly efficient concurrent systems that follow best practices.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Learning-Concurrency-in-Python-Video-.

Table of Contents

  1. Chapter 1 : Speed It Up!
    1. The Course Overview 00:03:30
    2. Threads and Multithreading 00:03:04
    3. Processes and Event-Driven Programming 00:04:08
    4. Concurrent Image Download 00:04:54
    5. Improving Number Crunching with Multiprocessing 00:05:25
  2. Chapter 2 : Parallelize It!
    1. Concurrency and I/O Bottlenecks 00:04:36
    2. Understanding Parallelism 00:01:46
    3. Computer Memory Architecture Styles 00:02:34
  3. Chapter 3 : Life of a Thread
    1. Threads in Python 00:05:37
    2. Starting a Thread 00:05:38
    3. Handling Threads in Python 00:10:03
    4. How Does the Operating System Handle Threads? 00:03:09
  4. Chapter 4 : Synchronization Between Threads
    1. Deadlocks and Race Condition 00:04:33
    2. Shared Resources and Data Races 00:07:02
    3. Conditions and Semaphores 00:08:45
    4. Events and Barriers 00:04:23
  5. Chapter 5 : Communication Between Threads
    1. Sets and Decorator 00:04:00
    2. Queues 00:06:10
    3. Queue and Deque Objects 00:06:17
    4. Appending, Popping, and Inserting Elements 00:04:31
    5. Defining Your Own Thread-Safe Communication Structures 00:06:35
  6. Chapter 6 : Debug and Benchmark
    1. Testing Strategies 00:04:42
    2. Debugging 00:01:33
    3. Benchmarking 00:05:02
    4. Profiling 00:10:59
  7. Chapter 7 : Executors and Pools
    1. Concurrent Futures 00:06:50
    2. Future Objects 00:05:48
    3. Setting Callbacks and Exception Classes 00:04:08
    4. ProcessPoolExecutor 00:04:37
    5. Improving Our Crawler 00:03:49
  8. Chapter 8 : Multiprocessing
    1. Working Around the GIL and Daemon Processes 00:04:02
    2. Identifying and Terminating Processes 00:05:24
    3. Multiprocessing Pools 00:09:32
    4. Communication Between Processes 00:03:44
    5. Multiprocessing Manager 00:07:41
    6. Communicating Sequential Processes 00:02:18
  9. Chapter 9 : Event-Driven Programming
    1. Event-Driven Programming 00:03:13
    2. Getting Started with Asyncio 00:16:00
    3. Debugging Asyncio Programs 00:02:24
    4. Twisted 00:02:43
    5. Gevent 00:04:24