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

Automatic vectorization for efficient kernels

You might remember from our discussions on NumPy that the library is efficient at applying numerical operations to all elements in an array or the elements along specific axes. By exploiting the fact that the same operation is to be applied to multiple elements, the library optimizes low-level code that performs the operation, making the computation much more efficient than doing the same thing via an iterative loop. This process is called vectorization.

When working with machine learning models, we would like to go through a procedure of vectorizing a specific function, rather than looping through an array or a matrix, to gain performance speedup. Vectorization is typically not easy to do and might involve clever tricks to rewrite the function that we'd like to vectorize into another form that admits vectorization easily.

JAX addresses this concern by providing a function transformation that automatically vectorizes a given...