AI in Rust

Engineering Programming

AI has become a popular topic in software development, and Rust has become a good programming language for making software that is efficient, safe, and reliable. This article will talk about how AI can be built with Rust and how the Rust community is making tools and libraries to help with AI workloads.

What is Rust?

Rust is a systems programming language that was first released in 2010 by Mozilla Research. Rust is designed to be fast, efficient, and safe, with a focus on memory safety and thread safety. Rust is becoming increasingly popular due to its features, including:

  • Memory safety: Rust’s ownership model and borrowing system prevent common memory errors like null pointer dereferences, use-after-free errors, and buffer overflows.
  • Performance: Rust provides control over low-level details like memory allocation and CPU instructions, allowing for high performance and low overhead.
  • Concurrency: Rust provides strong support for concurrency and parallelism, allowing for efficient use of multiple cores and threads.
  • Safety: Rust is designed to make it easy to write safe and secure code, preventing common errors and vulnerabilities.

Why use Rust for AI development?

AI development needs high-performance computing, and Rust is a good choice because it focuses on performance and parallelism. Also, Rust’s focus on safety and security is especially important in AI development, since AI models can have bad effects if they are not built and used safely.
Rust is also becoming more popular in the community of people who work with machine learning (ML). Many machine learning frameworks, like TensorFlow and PyTorch, have bindings for Rust that let developers use Rust for training and inference tasks. Rust is a good choice for making AI systems that need to work with other tools and platforms because it is easy to integrate with other languages and systems.

Using Rust for AI development

There are a number of tools and libraries that can be used with Rust to build AI. Here are some examples:

  • ndarray: ndarray is a Rust library for multidimensional arrays and linear algebra. ndarray provides a convenient and efficient way to work with arrays in Rust, and is often used in ML applications.
  • tch-rs: tch-rs is a Rust binding for PyTorch, a popular deep learning framework. tch-rs allows developers to use PyTorch from within Rust, and provides support for building and training deep learning models.
  • rustlearn: rustlearn is a machine learning library for Rust. rustlearn provides a variety of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for feature engineering and data preprocessing.
  • rusty-machine: rusty-machine is another machine learning library for Rust. rusty-machine provides implementations of several common ML algorithms, including linear regression, logistic regression, and k-nearest neighbors.

Example Code

Here’s an example of using Rust and tch-rs to build and train a simple neural network:

use tch::{nn, Tensor};
fn main() {
    // Create a tensor of input data
    let input = Tensor::of_slice(&[1., 2., 3., 4.]).reshape(&[2, 2]);
    // Define the neural network architecture
    let net = nn::Sequential::new()
        .add(nn::Linear::new(2, 3, Default::default()))
        .add_fn(|xs| xs.relu())
        .add(nn::Linear::new(3, 1, Default::default()));
    // Create a new optimizer
    let opt = nn::Adam::default().build(&net.parameters().unwrap());
    // Train the model for 100 epochs
    for epoch in 1..=100 {
        // Compute the output of the model
        let output = net.forward(&input);