Smartcore

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smartcore is an open-source machine learning and numerical computing library for Rust. It provides a comprehensive set of classical supervised and unsupervised methods for developers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is smartcore?

smartcore is a comprehensive, fast, and ergonomic open-source library designed for machine learning and numerical computing in Rust. It enables developers to apply machine learning algorithms leveraging first principles, covering a broad range of methods including linear models, tree-based methods, ensembles, SVMs, neighbors, clustering, decomposition, and preprocessing. The library emphasizes production-friendly APIs, strong typing, and good defaults, while remaining flexible for research and experimentation. It features strong linear algebra traits with optional ndarray integration, WASM-first defaults for portability, and practical utilities for model selection, evaluation, and data access. smartcore is ideal for developers building AI applications in Rust who need robust and efficient ML capabilities.

Best used for

Ideal for developers who need to implement classical machine learning algorithms, perform numerical computing, and build AI applications in Rust. Especially valuable for those requiring production-friendly APIs, strong typing, and portability with WASM-first defaults.

Common actions

implement machine learning

perform numerical computing

develop AI applications

experiment with ML models

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Capabilities

Key features

Machine learning algorithms
Numerical computing
Linear algebra traits
WASM-first defaults
Dataset generators
Model selection utilities

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of machine learning algorithms does smartcore support?

smartcore supports a broad range of classical supervised and unsupervised machine learning algorithms. This includes linear models, tree-based methods, ensembles, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), clustering algorithms like K-Means and DBSCAN, and matrix decomposition techniques such as SVD and EVD.

Is smartcore suitable for WebAssembly (WASM) deployments?

Yes, smartcore adopts a WASM/WASI-first posture in its defaults, making it well-suited for browser and embedded deployments. It pays attention to portability, structuring tests and IO utilities to avoid unsupported calls in WASM targets, and allows selective feature enablement to minimize footprint.

How can I get started with smartcore?

You can get started by adding smartcore to your Cargo.toml dependencies. For interactive exploration and familiarization with the v0.4 API, a Jupyter Notebook environment repository is available. This is the recommended path for quickly experimenting with the library.

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Data & Analytics › Predictive Analytics Research & Education › Scientific Computing Data & Analytics › Statistical & Scientific

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