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

Just-In-Time compilation for improved efficiency

As we have learned from the last chapter, JIT compilation allows a piece of code that is expected to run many times to be executed more efficiently. This process is specifically useful in machine learning where functions such as the loss or the gradient of the loss of a model need to be computed many times during the loss minimization phase. We hence expect that by leveraging a JIT compiler, we can make our machine learning models train faster.

You might think that to do this, we would need to hook one of the JIT compilers we considered in the last chapter into JAX. However, JAX comes with its own JIT compiler, which requires minimal code to integrate in an existing program. We will see how to use it by modifying the training loop we made in the last section.

First, we reset the parameters of our models:

np.random.seed(0)
w = np.random.randn(3)

Now, the way we will integrate the JIT compiler into our program is to point...