Book Image

Speed Up Your Python with Rust

By : Maxwell Flitton
5 (2)
Book Image

Speed Up Your Python with Rust

5 (2)
By: Maxwell Flitton

Overview of this book

Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in. This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust. By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.
Table of Contents (16 chapters)
1
Section 1: Getting to Understand Rust
5
Section 2: Fusing Rust with Python
11
Section 3: Infusing Rust into a Web Application

Building a model in NumPy

In this section, we are going to build a basic mathematical model to demonstrate the power that NumPy has apart from speed. We are going to use matrices to make a simple model. To achieve this, we will have to carry out the following steps:

  1. Define our model.
  2. Build a Python object that executes our model.

Let's look at these steps in detail in the following subsections.

Defining our model

A mathematical model is essentially a set of weights that calculate an outcome based on inputs. Before we go any further, we must remember the scope of this book. We are building a model to demonstrate how to utilize NumPy. If we covered the nuances of mathematical modeling, that would take up the whole book. We will be building a model based on the example discussed in the previous section, but this does not mean that the model defined is an accurate description of the complexity of mathematical modeling. Here are the steps we need to take...