Python Code Does Not Have to Be Slow!
(or run on just one CPU)
With "Python ProcessPoolExecutor Jump-Start" you will learn how to create parallel for-loops and execute asynchronous tasks in just 7 lessons.
How Much Faster Could Your Code Run
(if it used all CPU cores)?
Is this situation familiar to you...
You have a slow for-loop in your program that executes tasks one-by-one.
It waits for each task to complete before executing the next.
Yet, you have 2, 4, 8 or more CPU cores sitting idle.
Waiting for work.
What a waste!
What if you could use all of the CPU cores in your system right now, with just a very small change to your code.
- You could change a slow sequential for-loop into a blazingly-fast parallel for-loop.
- You could change run-and-wait tasks into fire-and-forget asynchronous tasks.
Imagine dropping run times down by 25%, 50% or more.
This is possible right now with a lesser-known Python class that offers super-easy-to-use process-based concurrency (and is already installed on your system).
The Path to Faster Python Code
Python is a joy to use, but getting Python code to run fast is challenging.
Concurrency is a standard approach to running multiple functions simultaneously.
Python concurrency has a bad reputation. So bad, that many believe Python does not support true concurrency.
I'm happy to say that these misconceptions are dead wrong.
Python supports real concurrency with first-class native support for thread-based and process-based concurrency.
It always has.
On all major platforms, like Windows, MacOS, and Linux.
And most importantly, Python concurrency is easy and fun to use.
The trick is to use process pools.
The Modern Class For Parallel Python
The ProcessPoolExecutor class provides easy-to-use process-based concurrency.
This is not some random third-party library, this is a class provided in the Python standard library (already installed on your system).
The ProcessPoolExecutor class has been in the standard library since Python 3.2.
This is the class you need to use to make your code run faster.
It is specifically designed for you to run for-loops in parallel and automatically use all of the CPU cores available in your system.
There's just one problem.
No one knows about it (or how to use it well).
- The API documentation is thin at best, providing very little guidance on how to use its features.
- The API docs are buried deep within the Python documentation, making it impossible to find.
- The few examples out there on the web are terse and years out of date.
This is madness!
The perfect class for faster parallel Python code is right there in the standard library, and very few people know it exists or can locate it, let alone decipher the API documentation.
One group of Python developers know about this class and how to use it well.
Python machine learning developers.
My Story: Who Is Jason Brownlee?
(from Python ML engineer to Python concurrency expert and evangelist)
Hi, I'm Jason Brownlee, Ph.D.
I'm a Python developer, husband, and father to two boys.
I want to share something with you.
I am obsessed with Python concurrency, but I wasn't always this way.
My background is in Artificial Intelligence and I have a few fancy degrees and past job titles to prove it.
You can see my LinkedIn profile here:
- Jason Brownlee on LinkedIn
(go ahead and follow me if you like)
Well, AI and machine learning has been hot for the last decade. I have spent that time as a Python machine learning developer:
- ... working on a range of modeling projects.
- ... writing more than 1,000+ tutorials.
- ... and authoring over 20 books.
There's one thing about machine learning in Python: your code must be fast.
Modeling code is already generally fast, built on top of C code libraries and such.
But you know how it is on real projects…
You always have to glue bits together, wrap the fast code and run it many times, and so on.
Making code run fast requires Python concurrency and I have spent most of the last decade using all the different types of Python concurrency available.
Including threading, multiprocessing, asyncio, and the suite of popular libraries.
I know my way around Python concurrency and I am deeply frustrated at the bad wrap it has.
This is why I started SuperFastPython.com where I share hundreds of free tutorials on Python concurrency.
And this is why I wrote this book.
A new Ebook designed to teach you the ProcessPoolExecutor in Python, super fast!
You don't want to read a book or take a book.
You want faster Python code, and you want it yesterday.
I hear you.
This is not a textbook on Python Concurrency.
It's not a technical thesis on the internals of process-based concurrency in Python.
Instead, it's what you actually need.
It's a fast-paced, 7-part ebook to you get started and get good at using the ProcessPoolExecutor, super fast.
Each of the 7 lessons was carefully designed to teach one critical aspect of the ProcessPoolExecutor, with explanations, code snippets and worked examples.
Each lesson ends with an exercise for you to complete to confirm you understood the topic, a summary of what was learned, and links for further reading if you want to go deeper.
Next, let's take a closer look at the 7-lessons in the book.
So What Are The Lessons?
This book is designed to bring you up-to-speed with how to use the ProcessPoolExecutor as fast as possible.
As such, it is not exhaustive.
There are many topics that are interesting or helpful, that are not on the critical path to getting you productive.
This book is divided into 7 lessons, they are:
- Lesson 01: Processes, Executors, and Process Pools
- Lesson 02: Configure the
- Lesson 03: Execute Multiple Tasks Concurrently
- Lesson 04: Execute One-Off Tasks Asynchronously
- Lesson 05: Query Asynchronous Tasks
- Lesson 06: Manage Collections of Asynchronous Tasks
- Lesson 07: Case Study Calculate Fibonacci Numbers
Next, let's look at the structure of the lessons.
So What is the Structure of Each Lesson?
(lesson and review with exercises)
Each lesson has two main parts, they are:
- The body of the lesson (code tutorials)
- The lesson overview (refs and exercises)
The body of the lesson will introduce a topic with code examples, whereas the lesson overview will review what was learned with exercises and links for further information.
Each lesson has a specific learning outcome and is designed to be completed in 10-to-20 minutes.
Each lesson is also designed to be self-contained so that you can read the lessons out of order if you choose, such as dipping into topics in the future to solve specific programming problems.
We Python developers learn best from real and working code examples. So each lesson has multiple large worked examples with sample output.
So, after completing the book, what will you know?
What You Will Know After Finishing
Transform From "Python Developer" to "Python Developer Who Can Write Python Code to Run on All Cores"
This book will transform you from a Python developer into a Python developer that can confidently bring concurrency to your projects with the ProcessPoolExecutor.
After working through all of the lessons in this book, you will know:
- How to create a
ProcessPoolExecutorto execute CPU-bound tasks concurrently.
- How to configure the
ProcessPoolExecutorincluding inspecting the default configuration, how to set the number of worker processes, process start method, and how to initialize worker processes.
- How to execute multiple tasks concurrently using the
- How to issue asynchronous tasks to the
- How to query, get results, handle exceptions and use callback functions with
Futureobjects for asynchronous tasks.
- How to manage collections of asynchronous tasks issued to the
ProcessPoolExecutorincluding how to handle results in task completion order and wait for all tasks or for the first task to complete.
- How to build upon what you have learned to speed-up the CPU-bound task of calculating Fibonacci numbers by calculating them in parallel then further optimizing the parallel execution of the program.
How Long Will It Take To Finish?
(a few hours, or a week)
You will need about 20-to-30 minutes per lesson, so a few hours total.
But that's too fast.
Instead, I recommend 1 lesson per day, over 7 days (1 week).
Work at your own pace.
There's no rush and I recommend that you take your time.
The book is designed to be read linearly from start to finish, guiding you from being a Python developer at the start of the book to being a Python developer that can confidently use the ProcessPoolExecutor in your project by the end of the book.
In order to avoid overload, I recommend completing one or two lessons per day, such as in the evening or during your lunch break. This will allow you to complete the transformation in about one week.
I recommend you maintain a directory with all of the code you type from the lessons in the book. This will allow you to use the directory as your own private code library, allowing you to copy-paste code into your projects in the future.
I recommend trying to adapt and extend the examples in the lessons. Play with them. Break them. This will help you learn more about how the API works and why we follow specific usage patterns.
What Format Is the Ebook?
(pdf and epub)
The ebook is provide in 2 formats:
- PDF (.pdf): perfect for reading on the screen or tablet.
- EPub (.epub): perfect for reading on a tablet with a Kindle or iBooks app.
Who Is This Book For?
Before you pull the trigger, let's make sure it is a good fit for you.
This book is designed for Python developers who want to discover how to use and get the most out of the ProcessPoolExecutor class for executing Python code in Parallel.
Specifically, this book is for:
- Developers that can write simple Python programs.
- Developers that need better performance from current or future Python programs.
- Developers that are working with CPU-bound tasks.
This book does not require that you are an expert in the Python programming language or concurrency.
- You do not need to be an expert Python developer.
- You do not need to be an expert in concurrency.
What Version of Python is Used?
All code examples use Python 3.
Python 3.9+ to be exact.
Python 2.7 is not supported because it reached end of life in 2020.
Are There Code Examples?
There are 31 .py files.
Each lesson has one or more complete, standalone, and fully-working code examples.
The book is provided in a .zip file that includes a src/ directory containing all source code files used in the book.
How Many Pages Is The Book?
The PDF is 75 US letter size pages.
Can I Print The Book?
Although, I think it's better working through it on the screen.
- You can search, skip, and jump around really fast.
- You can copy-and-paste code examples.
- You can compare code output directly.
Is There Digital Rights Management (DRM)?
What if I Need Help?
The lessons were designed to be easy to read and follow.
Nevertheless, sometimes we need a little extra help.
A list of further reading resources is provided at the end of each lesson. These can be helpful if you are interested in learning more about the topic covered, such as fine grained details of the standard library and API functions used.
The conclusions at the end of the book provide a complete list of websites and books that can help if you want to learn more about Python concurrency and the relevant parts of the Python standard library. It also lists places where you can go online and ask questions about Python concurrency.
Finally, if you ever have questions about the lessons or code in this book, you can contact me any time and I will do my best to help. My contact details are provided at the end of the book.
Do I Get Free Updates?
Each time I release an updated version, I will send you an email with a link so that you can download the latest version for free.
I want you to be happy, and I stand behind my materials.
If you decide that Python concurrency is not for you, or whatever reason, I'll understand.
I offer a 100% money back guarantee, no questions asked.
Get in touch at:
Can I Buy The Book Elsewhere?
You can get a kindle or paperback version from Amazon.
Is There a Paperback Version?
You can get a paperback version from Amazon.
Can I Read a Sample?
You can read a book sample via google books "preview" or via the amazon "look inside" feature:
Generally, if you like my writing style on SuperFastPython, then you will like the books.
Can I Download the Source Code Now?
The source code (.py) files are included in the .zip with the books.
Nevertheless, you can also download all of the code from the dedicated GitHub Project:
Does it Work on My OS?
Python parallelism is built into the Python programming language and works equally well on Windows, MacOS and Linux.
Does it Work on My Hardware?
Python parallelism is agnostic to the underlying CPU hardware.
If you are running Python on a modern computer, then you will have support for parallelism, e.g. Intel, AMD, and ARM/Apple Silicon CPUs are supported.
How do the Jump-Start Books Compare to the Guides?
The SuperFastPython Jump-Start books are laser-focused on making you productive with a Python concurrency module or class as fast as possible.
This means that many broader topics are not covered because they are not on the critical path.
The guides on SuperFastPython.com are broader in scope and compare the class or module to related modules, describing best practices, common errors, common usage questions, and common objects.
This material may be interesting but is a distraction when you are focused on getting productive as fast as possible.
Another important difference is that the jump-start books are provided in book form, whereas the guides are very long web pages.
This makes the books easy to read on a kindle, tablet, or paperback, as well as the screen, whereas the guides must be read in the browser.
Contact me directly, any time about this book or Python concurrency generally.
I'm here to help as best I can.
You can send an email directly to my inbox via:
Praise for Super Fast Python
What Are You Waiting For?
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Learn Python concurrency correctly, step-by-step.
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You'll get a .zip download containing both ebook formats (pdf and epub) and all .py code files.