Concurrent and Parallel Programming in Python

Concurrent and Parallel Programming in Python, available at $59.99, has an average rating of 4.39, with 28 lectures, based on 423 reviews, and has 3773 subscribers.

You will learn about How to use concurrency and parallelism in Python How to write multi-threaded programs How to write multi-process programs How to write asynchronous programs This course is ideal for individuals who are Python developers that want to make their programs faster by adding concurrency It is particularly useful for Python developers that want to make their programs faster by adding concurrency.

Enroll now: Concurrent and Parallel Programming in Python

Summary

Title: Concurrent and Parallel Programming in Python

Price: $59.99

Average Rating: 4.39

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

How to use concurrency and parallelism in Python
How to write multi-threaded programs
How to write multi-process programs
How to write asynchronous programs

Who Should Attend

Python developers that want to make their programs faster by adding concurrency

Target Audiences

Python developers that want to make their programs faster by adding concurrency

In this course you’ll learn how to create multi-threaded, asynchronous, and multi-process programs in Python, so that you can make your programs run even faster.

In applications communicating with other resources, a lot of time is spent just waiting for information to be passed from one place to another. You’ll learn how to use multi-threading as well as asynchronous programming to speed up programs that are heavily bottlenecked by IO operations.

We’ll go through an introduction first of where potential speed bottlenecks come from as well as how we could solve these issues, and then we’ll dive directly into the technical content and build out a multi-threaded program together that grabs data from the internet, parses, and saves it into a local database.

Other programs may be more heavily affected by CPU limitations. We’ll also learn how to implement multiprocessing in Python, the library that lets us use multiple CPUs in our Python code. With this we’ll be able to spread our workload over all the cores available on the machine we’re using.

Finally, we’ll also look to combine both elements, taking a look at how we can use multiprocessing together with asynchronous programming to get the most benefit for yourself, maximizing your use of CPU resources and minimizing time spent siting idle waiting for IO response.

You can find the lecture code in the GitHub repository linked in the first lesson.

Course Curriculum

Chapter 1: Threading

Lecture 1: Threading, Multiprocessing, Async Intro

Lecture 2: Threading in Python

Lecture 3: Creating a Threading Class

Lecture 4: Creating a Wikipedia Reader

Lecture 5: Creating a Yahoo Finance Reader

Lecture 6: Queues and Master Scheduler

Lecture 7: Creating a Postgres Worker

Lecture 8: Integrating the Postgres Worker

Lecture 9: Yaml File Intro

Lecture 10: Creating a Yaml Reader

Lecture 11: Improving Our Wiki Worker

Lecture 12: Improving All Workers and Adding Monitoring

Lecture 13: Final Program Cleanup

Lecture 14: Locking

Chapter 2: Multiprocessing

Lecture 1: Multiprocessing Intro

Lecture 2: Multiprocessing Queues

Lecture 3: Multiprocessing Pool

Lecture 4: Multiprocessing Pool Map Multiple Arguments

Lecture 5: Multiprocessing Multiple Varying Arguments

Lecture 6: Multiprocessing Checking Elements in List in Certain Ranges

Chapter 3: Asynchronous

Lecture 1: Intro to Writing Asynchronous Programs

Lecture 2: Asynchronous Tasks

Lecture 3: Async Gather Method

Lecture 4: Using Async Timeouts

Lecture 5: Creating Asynchronous For Loops

Lecture 6: Using Asynchronous Libraries

Lecture 7: The Async Wait Statement

Lecture 8: Combining Async and Multiprocessing

Instructors

Max S
Data Engineer

Rating Distribution

1 stars: 12 votes
2 stars: 8 votes
3 stars: 62 votes
4 stars: 146 votes
5 stars: 196 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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