Python Concurrency & Parallel Programming
Learning Path ⋅ Skills: Multithreading, Multiprocessing, Async IO
With this learning path you’ll gain a deep understanding of concurrency and parallel programming in Python. You can use these newfound skills to speed up CPU or IO-bound Python programs.
Python Concurrency & Parallel Programming
Learning Path ⋅ 9 Resources
Course
Speed Up Python With Concurrency
Learn what concurrency means in Python and why you might want to use it. You'll see a simple, non-concurrent approach and then look into why you'd want threading, asyncio, or multiprocessing.
Course
Hands-On Python 3 Concurrency With the asyncio Module
Learn how to speed up your Python 3 programs using concurrency and the asyncio module in the standard library. See step-by-step how to leverage concurrency and parallelism in your own programs, all the way to building a complete HTTP downloader example app using asyncio and aiohttp.
Course
Threading in Python
In this intermediate-level course, you'll learn how to use threading in your Python programs. You'll see how to create threads, how to coordinate and synchronize them, and how to handle common problems that arise in threading.
Tutorial
Getting Started With Async Features in Python
This step-by-step tutorial gives you the tools you need to start making asynchronous programming techniques a part of your repertoire. You'll learn how to use Python async features to take advantage of IO processes and free up your CPU.
Tutorial
What Is the Python Global Interpreter Lock (GIL)?
Python's Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. In this article you'll learn how the GIL affects the performance of your Python programs.
Tutorial
Bypassing the GIL for Parallel Processing in Python
In this tutorial, you'll take a deep dive into parallel processing in Python. You'll learn about a few traditional and several novel ways of sidestepping the global interpreter lock (GIL) to achieve genuine shared-memory parallelism of your CPU-bound tasks.
Course
Parallel Map Reduce in Python
How to do parallel processing with multiprocessing and concurrent.futures using a functional programming map-reduce approach.
Tutorial
First Steps With PySpark and Big Data Processing
Take your first steps with Spark, PySpark, and Big Data processing concepts using intermediate Python concepts.
Tutorial
NumPy Vectorization & Array Programming
How to take advantage of vectorization and broadcasting so you can use NumPy to its full capacity. In this tutorial you'll see step-by-step how these advanced features in NumPy help you writer faster code.
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