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Learning Concurrency in Python

Learning Concurrency in Python

By : Forbes
3.3 (3)
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Learning Concurrency in Python

Learning Concurrency in Python

3.3 (3)
By: Forbes

Overview of this book

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.
Table of Contents (13 chapters)
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Numba

The Numba Python compiler from continuum analytics helps make highly parallelizable, incredibly powerful performance from an interpreted language a reality.

Note: The documentation on the official pydata website provides a comprehensive overview of what Numba is and how you can leverage it in your own Python programs. You can find it at http://numba.pydata.org/#.

In this section, we'll have a look at the ecosystem surrounding Numba, which takes its form in the shape of Anaconda. We'll also look at how you can then leverage Numba alongside numerous other packages in order to effectively and efficiently perform analysis of big data. We'll cover some of the basics of Numba and then work our way into the more complex aspects such as utilizing GPUs and APUs within our program.

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