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    Détails du Torrent Pour "Asynchronous Programming with Python Write, Test, and Debug Robust Asynchronous Code"

    Asynchronous Programming with Python Write, Test, and Debug Robust Asynchronous Code

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    Dernière vérification: 08-01-2021 16:32:42

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    _NAME_:Asynchronous Programming with Python Write, Test, and Debug Robust Asynchronous Code
    Description:

    Description

    In this course, we will look at using asynchronous programming in Python: the options, pitfalls, and best practices. We start with multi-threading, which is particularly useful when there is a lot of waiting, e.g. for HTTP requests or disk access. With multi-threading, you can start many requests in quick succession and then wait for all of them to complete at once. Next, the course will show you how to write your code in a thread-safe manner, and how to use it risk-free. Further, it covers Python’s global interpreter lock, which prevents a lot of serious problems in Python but also stops you from running threads in parallel.
    Going forward we discover how you can use Python’s multiprocessing library to run functions in parallel. Threads and processes often need to share or exchange data. In asynchronous code just passing Python objects is usually not the safest way to do this. This course looks at the main ways to do this correctly in Python, such as queues and events. Finally, the course moves on to the concurrent.future library which contains higher-level abstractions, including thread and processing pools and an asynchronous map function.

    The course finishes with advice on how to write robust asynchronous code, and how to test and debug it.

    What You Will Learn

       Take advantage of multi threading (concurrency) using the threading module
       Implement multi processing (parallelism) using the multiprocessing module
       Understand inter-process communication and data sharing using locks, queues, semaphores, barriers, events, and timers
       Test and debug asynchronous Python code
       Understand thread and process pools using concurrent.futures
       Who This Video Is For
       Intermediate to experienced Python programmers who want to speed up existing Python code by using multi processing and multi threading.
    YouTube Video:
    Catégorie:Tutorials
    Langue :English  English
    Taille totale:237.06 MB
    Info Hash:F9FB965835E998B0A3F13ED718AF389542236BD7
    Ajouté par:tutsnode Verified UploaderVIP
    Date:2021-01-09 00:32:39
    Statut Torrent:Torrent Verified


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