Rust for Machine Learning: Opportunities and Limitations for Beginners

Rust for Machine Learning: Is It Worth Starting from Scratch?

The world of machine learning (ML) has long been associated exclusively with Python. However, in recent years, the question "Can Rust be used for ML?" is being asked more and more often. Rust is a young but rapidly growing language, known for its speed and safety. In this article, we will analyze what opportunities Rust opens up for machine learning tasks, what limitations a beginner will face, and whether it is worth learning it alongside Python.

This article is intended for beginner developers who are already familiar with the basics of programming and want to understand where Rust can be useful in ML.

Key Opportunities of Rust for Machine Learning

Rust is a systems language that compiles to native code. This gives it several key advantages for ML:

• Performance: Rust operates at the level of C/C++, which is critical for processing large volumes of data and training models.
• Memory Safety: The ownership and borrowing system eliminates entire classes of errors that often occur in Python (e.g., memory leaks or race conditions in multithreading).
• Fearless Concurrency: Rust makes it easy to write safe multithreaded code. This is ideal for parallelizing computations, for example, when training neural networks.

For ML in Rust, there are several mature libraries. Here are the most popular ones:

• ndarray — an analogue of NumPy for working with multidimensional arrays.
• linfa — a framework for classical ML algorithms (linear regression, SVM, clustering).
• tch-rs — Rust bindings to the PyTorch (Torch) library.
• candle — a minimalist ML framework from Hugging Face, written in Rust.

Example: Linear Regression in Rust with linfa

Let's look at a simple example — training a linear regression on synthetic data. For this, we will need the crates linfa, ndarray, and rand.

use linfa::prelude::*;use ndarray::Array2;use rand::Rng;

fn main() {    // Generate random data    let mut rng = rand::thread_rng();    let mut x: Vec<f64> = Vec::new();    let mut y: Vec<f64> = Vec::new();    for _ in 0..100 {        let xi = rng.gen_range(0.0..10.0);        x.push(xi);        y.push(2.0 * xi + 1.0 + rng.gen_range(-0.5..0.5)); // y = 2x + 1 + noise    }

    // Convert to matrix    let x_matrix = Array2::from_shape_vec((100, 1), x).unwrap();    let y_array = ndarray::Array1::from_vec(y);

    // Create dataset    let dataset = Dataset::new(x_matrix, y_array);

    // Train the linear regression model    let model = linfa_linear::LinearRegression::fit(&dataset, Default::default()).unwrap();

    // Print parameters    println!("Coefficients: {:?}", model.params());    println!("Intercept: {:?}", model.intercept());}

As you can see, the code is quite concise, although it requires explicit type annotations. This is an example of how Rust can be used for classical ML tasks.

Limitations of Rust for Machine Learning

Despite its power, Rust has a number of significant limitations that are especially noticeable for beginners:

• Steep learning curve. The ownership, borrowing, and lifetime system are complex concepts. Mastering them will take significantly more time than learning Python.
• Small number of libraries. The Rust ecosystem for ML is still in its infancy. You won't find ready-made solutions for transformers, generative models, or complex neural networks. Keras, TensorFlow, PyTorch — all of these are only available through bindings, which often lag behind