Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Summary

Parallel processing is an effective way to improve performance on large datasets. Embarrassingly parallel problems are excellent candidates for parallel execution that can be easily implemented to achieve good performance scaling.

In this chapter, we illustrated the basics of parallel programming in Python. We learned how to circumvent Python threading limitations by spawning processes using the tools available in the Python standard library. We also explored how to implement a multithreaded program using Cython and OpenMP.

For more complex problems, we learned how to use the Theano, TensorFlow, and Numba packages to automatically compile array-intensive expressions for parallel execution on CPU and GPU devices.

In the next chapter, we will learn how to apply parallel programming techniques to build a hands-on application that makes and handles web requests concurrently.