Rust for Machine Learning: Capabilities and Limitations

Introduction: Rust in the World of Machine Learning

The world of machine learning (ML) has long been monopolized by Python — thanks to its simplicity, abundance of libraries (NumPy, scikit-learn, PyTorch, TensorFlow), and huge community. However, in recent years, voices have increasingly been heard that Rust could become a serious competitor in this field.

Rust is a systems programming language that combines the performance of C/C++ with memory safety at the compilation level. It promises speed, reliability, and control over resources. But is this enough for ML? In this article, we will thoroughly examine what capabilities Rust offers for machine learning, what limitations developers face, and whether it is worth switching from the familiar Python.

Why Rust Might Be Interesting for ML?

Performance and Memory Control

One of the main advantages of Rust is speed. Thanks to compilation into native code and the absence of a garbage collector, Rust programs can run many times faster than their Python counterparts. This is critical for tasks that require processing large volumes of data or training models in real time.

Furthermore, Rust guarantees memory safety at the compilation stage. Errors like segmentation fault or memory leaks, which are common in C/C++, are practically impossible in Rust. For ML infrastructure where reliability is critical (for example, in autonomous systems or financial algorithms), this is a huge plus.

Concurrency and Multithreading

Modern ML models require parallel computations. Rust provides powerful tools for working with threads (std::thread) and asynchronous programming (tokio, async-std). At the same time, the ownership and borrowing system prevents data races at the compilation stage — something that in Python or C++ has to be caught at runtime.

use std::thread;

fn main() {    let mut handles = vec![];

    for i in 0..4 {        handles.push(thread::spawn(move || {            println!("Thread {} started computation", i);            // Here could be gradient computation or data preprocessing            i * i        }));    }

    for handle in handles {        println!("Result: {}", handle.join().unwrap());    }}

Rust Ecosystem for Machine Learning

Linfa — an analog of scikit-learn

Linfa is a framework for classical machine learning in Rust. It provides algorithms for classification, regression, clustering, dimensionality reduction, and data preprocessing. By design, Linfa aims to be what scikit-learn is for Python.

Example of linear regression with Linfa:

use linfa::prelude::*;use linfa_linear::LinearRegression;use ndarray::Array2;

fn main() {    // Data: feature matrix (X) and target variable (y)    let x = Array2::from_shape_vec((5, 2), vec![1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0, 6.0]).unwrap();    let y = vec![5.0, 7.0, 9.0, 11.0, 13.0];

    let dataset = Dataset::new(x, y);    let model = LinearRegression::default().fit(&dataset).unwrap();

    println!("Coefficients: {:?}", model.params());}

Currently, Linfa supports about 20 algorithms — still far from everything available in scikit-learn, but the foundation has been laid.

Burn — neural networks in Rust

Burn is a modern deep learning framework written in Rust. It supports automatic differentiation, GPU operation (via WGPU and CUDA), and has a modular architecture. Burn is actively developed and already allows creating and training neural networks.

Example of a simple neural network in Burn:

use burn::tensor::{Tensor, backend::Backend};use burn::nn::{Linear, Relu};

struct MyModel<B: Backend> {    layer1: Linear<B>,    activation: Relu,