Python Concurrent Programming: Asynchronous Executions in Python

Python    |    Beginner
  • 9 videos | 1h 1m 59s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 24 users Rating 4.4 of 24 users (24)
This 9-video course offers a lab-only exploration and introduction to the libraries available in Python to run tasks asynchronously by using both processes and threads. To take this course, you should have prior knowledge of how to spawn and manage processes in Python. First, you will learn how to significantly improve the performance and responsiveness of your application by running them concurrently, then learn how to create a process pool, and how to use multiprocessing to execute tasks in parallel. Next, you will learn to use multithreading to run chunks of a task at one time, and to switch between the chunks regularly. Learners will then examine the concurrent.futures module, which contains objects to run threads and processes in an asynchronous manner, and to monitor their progress while they are still executing. Continue by learning how to use ThreadPoolExecutor, available in the concurrent.futures module. Finally, you examine the asyncio module in Python, which provides lightweight mechanisms for asynchronous executions of tasks.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Create a pool of processes to which tasks can be submitted
    Use the map function of a multithreading.pool instance to submit multiple tasks to a process pool
    Recognize the use of futures objects to execute tasks asynchronously
    Compare the performance of process pools and thread pools for tasks that are network-bound
  • Compare the performance of process pools and thread pools for tasks which are cpu-bound
    Create and execute a coroutine using the asyncio module
    Use the run, create_task, and gather functions in the asyncio modules to execute tasks
    Summarize process pools and contrast multithreading and multiprocessing in python

IN THIS COURSE

  • 2m 36s
  • 8m 15s
    In this video, learn how to create a pool of processes to submit tasks to. FREE ACCESS
  • Locked
    3.  Process Pools in Python - Part 2
    6m 50s
    In this video, you will use the map function of a multithreading.Pool instance to submit multiple tasks to a process pool. FREE ACCESS
  • Locked
    4.  Introducing the concurrent.futures module
    9m 20s
    Upon completion of this video, you will be able to recognize the use of futures objects to execute tasks asynchronously. FREE ACCESS
  • Locked
    5.  Threads vs. Processes for Network-bound Tasks
    9m 8s
    In this video, you will compare the performance of process pools and thread pools for tasks that are network-based. FREE ACCESS
  • Locked
    6.  Threads vs. Processes for CPU-bound Tasks
    5m 15s
    In this video, you will compare the performance of process pools and thread pools for tasks which are CPU-bound. FREE ACCESS
  • Locked
    7.  Introducing the asyncio Module
    5m 33s
    In this video, you will learn how to create and execute a coroutine using the asyncio module. FREE ACCESS
  • Locked
    8.  Concurrent Execution Using the asyncio Module
    9m 4s
    In this video, find out how to use the run, create_task, and gather functions in the asyncio module to execute tasks. FREE ACCESS
  • Locked
    9.  Exercise: Asynchronous Executions in Python
    5m 59s
    In this video, you will learn how to summarize process pools and contrast multithreading and multiprocessing in Python. FREE ACCESS

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