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  • Date: 12-12-2019, 04:56
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Talk Python Async Techniques And Examples In Python

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Talk Python   Async Techniques And Examples In Python
Talk Python - Async Techniques and Examples in Python
English | Size: 1.81 GB
Category: CBTs


Course Summary
Python's async and parallel programming support is highly underrated. In this course, you will learn the entire spectrum of Python's parallel APIs. We will start with covering the new and powerful async and await keywords along with the underpinning module: asyncio. Then we'll move on to Python's threads for parallelizing older operations and multiprocessing for CPU bound operations. We'll close out the course with a host of additional async topics such as async Flask, task coordination, thread safety, and C-based parallelism with Python.

Source code and course GitHub repository
What's this course about and how is it different?

This is *the* definitive course on parallel programming in Python. It covers the tried and true foundational concepts such as threads and multiprocessing as well as the most modern async features based on Python 3.7+ with async and await.
In addition to the core concepts and APIs for concurrent programming, you will learn best practices and how to choose between the various APIs as well as how to use them together for the biggest advantage.
In this course, you will:
See how concurrency allows improved performance and scalability
Build async-capable code with the new async and await keywords
Add asynchrony to your app without additional threads or processes
Work with multiple threads to run I/O bound work in Python
Use locks and thread safety mechanisms to protect shared data
Recognize a dead-lock and see how to prevent them in Python threads
Take full advantage of multicore CPUs with multiprocessing
Unify the thread and process APIs with execution pools
Add massive speedups with Cython and Python threads
Create async view methods in Flask web apps
And lots more
View the full course outline
Who is this course for?
Anyone who would like to write Python code that does more, scales better, and takes better advantage of modern, multicore CPUs. Whether you're a web developer or data scientists, you will find a host of techniques to do more faster.
The course is not a beginner Python course, so students with little to no Python language experience should take a foundational course first. We recommend our Python Jumpstart by Building 10 Apps as a prerequisite if needed.

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Tags: Python, Techniques, Examples, Python

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