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

Recreating our NumPy model in Rust

Now that we can use our NumPy module in Rust, we need to explore how to structure it so that we can use Python modules to solve bigger problems. We will do this by building a NumPy model with a Python interface. To achieve this, we can break down the processes into functions that can be used as and when we need them. The structure of our NumPy model can be seen here:

Figure 7.6 – Rust NumPy model structure  

Considering the flow of our model structure in the preceding diagram, we can build our NumPy model in Rust with the following steps:

  1. Build get_weight_matrix and inverse_weight_matrix functions.
  2. Build get_parameters, get_times, and get_input_vector functions.
  3. Build calculate_parameters and calculate_times functions.
  4. Add calculate functions to the Python bindings and add a NumPy dependency to our setup.py file.
  5. Build our Python interface.

We can see that each step has dependencies...