Rust-Based Machine Learning Tools
Rust combines the speed of C++ with the memory safety of modern languages, making it an emerging powerhouse for machine learning and numerical computing. Developers use it to build high-performance, crash-resistant systems that can run on everything from servers to embedded devices.
The Rust ML ecosystem is still growing, but it already offers powerful libraries for classical machine learning, deep learning, and scientific computing. These tools are ideal for projects that require speed, stability, and control, such as edge devices, robotics, and distributed systems.
| Tool / Library | Description | Key Features | Link |
|---|---|---|---|
| linfa | Crate ecosystem for classical machine learning including clustering, regression, and classification. | Modular design; integrates with ndarray for numerical operations. | crates.io/crates/linfa |
| tch-rs | Rust bindings for PyTorch allowing native training and inference of deep neural networks. | Tensor operations; CUDA and GPU acceleration support. | crates.io/crates/tch |
| Burn | Flexible deep learning framework written entirely in Rust for full control and performance. | Backend-agnostic (CPU or GPU); automatic differentiation for training models. | burn.dev |
| RustLearn (rustlearn) | Lightweight library offering native algorithms such as SVMs and decision trees. | Simple API; no foreign dependencies; ideal for learning or small projects. | crates.io/crates/rustlearn |
| ndarray | N-dimensional array library forming the foundation of numerical and ML work in Rust. | Linear algebra operations; multi-threaded computation and broadcasting. | crates.io/crates/ndarray |
| smartcore | Comprehensive ML library covering classical algorithms such as ensembles, SVM, and KNN. | Pure Rust implementation; no_std support for embedded applications. |
crates.io/crates/smartcore |