Rust for Machine Learning: Capabilities and Limitations

Can Rust become a new platform for machine learning?

The world of machine learning (ML) has long been monopolized by Python. Its ecosystem (TensorFlow, PyTorch, scikit-learn) has made ML accessible to millions of developers. However, with growing demands for performance and reliability in production systems, the question is increasingly being asked: what about Rust?

Rust is a systems programming language that offers the speed of C/C++ with guaranteed memory safety. In this article, we will explore why Rust might be interesting for ML, what tools already exist, and what limitations you will encounter.

Why Rust? Key advantages for ML

1. Performance without compromises

Rust compiles to machine code (LLVM), providing performance comparable to C++. This is critical for ML: model training, data preprocessing, and inference require maximum speed. Python, on the other hand, is interpreted, and its bottleneck is often the GIL (Global Interpreter Lock).

Example: a matrix multiplication operation in Rust can be 10–50 times faster than in pure Python, especially when using optimized libraries:

use ndarray::Array2;

fn main() {    let a = Array2::<f64>::ones((1000, 1000));    let b = Array2::<f64>::ones((1000, 1000));    let c = a.dot(&b);    println!("Result: {}", c[[0, 0]]);}

2. Memory safety at the compiler level

Errors in ML systems can be costly: a wrong pointer leads to a crash, and a memory leak leads to server downtime. Rust catches such errors at compile time. This is especially important for production systems where downtime is unacceptable.

3. Parallelism without data races

Rust encourages safe parallelism. The ownership system guarantees that you won't get a data race during training on multiple cores or GPUs.

Rust ecosystem for ML: frameworks and libraries

Although Rust is younger than Python, its ecosystem is actively growing. Here are the key projects:

• Burn — a modern deep learning framework. It supports automatic differentiation, GPU work (CUDA, Vulkan), and resembles PyTorch in API.
• Candle — a minimalist framework from Hugging Face. Lightweight, fast, with no Python dependencies. Ideal for inference.
• Linfa — an analogue of scikit-learn for Rust. Contains algorithms: linear regression, SVM, clustering (K-Means), PCA.
• ndarray — a basic library for working with N-dimensional arrays. An analogue of NumPy, but faster.

Example: a simple neural network in Burn

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

struct MyModel<B: Backend> {    layer1: Linear<B>,    layer2: Linear<B>,}

impl<B: Backend> MyModel<B> {    fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {        let x = self.layer1.forward(input);        let x = Relu::forward(x);        self.layer2.forward(x)    }}

As you can see, the code resembles PyTorch, but compiles into a native binary without an interpreter.

Limitations of Rust for machine learning

Despite all the advantages, Rust is not yet ready to completely replace Python in ML. Here are the main problems:

1. Small community and lack of libraries

For Python, there are thousands of ready-made models (Hugging Face Hub), pre-trained weights, and plugins. In Rust, this is just emerging. You will have to write many things from scratch or port code from Python.

2. Steep learning curve

Rust is difficult. Fighting with the borrow checker can slow down prototype development. For ML, where iteration speed is important, this is a disadvantage. Python allows you to test a hypothesis in an hour,