rust-ml/linfa

★ 4,650RustAudience · developerComplexity · 3/5LicenseSetup · moderate

Mindmap

mindmap
  root((linfa))
    What it does
      ML toolkit for Rust
      scikit-learn equivalent
    Algorithms
      Linear regression
      Decision trees
      K-means
      SVM
    Features
      BLAS backends
      WebAssembly support
    Audience
      Rust developers

mindmap root((linfa)) What it does ML toolkit for Rust scikit-learn equivalent Algorithms Linear regression Decision trees K-means SVM Features BLAS backends WebAssembly support Audience Rust developers

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Things people build with this

USE CASE 1

Build a classification or regression model in a Rust application without calling a Python service

USE CASE 2

Run K-means clustering or PCA dimensionality reduction on data directly from a Rust binary

USE CASE 3

Use WebAssembly to run machine learning inference in the browser from a Rust project

USE CASE 4

Add a random forest or SVM to a performance-critical Rust app where Python is not an option

Tech stack

RustBLASLAPACKOpenBLASIntel MKLWebAssembly

Getting it running

Difficulty · moderate Time to first run · 30min

Optional BLAS/LAPACK backends (OpenBLAS, Intel MKL) require system library installation, pure-Rust mode needs no extras.

Free to use for any purpose, including commercial use. You may choose either the MIT or Apache 2.0 license terms.

In plain English

Linfa is a machine learning toolkit written in Rust. It is modeled after Python's scikit-learn, a well-known library that provides a standard set of algorithms for classification, regression, clustering, and data preparation. Linfa aims to offer that same collection of everyday machine learning tools, but for developers working in Rust instead of Python. Rust is a programming language known for being fast and memory-safe, which makes it appealing for performance-critical applications. The trade-off is that its ecosystem for scientific computing and machine learning is less mature than Python's. Linfa is a community effort to close that gap for classical algorithms, meaning traditional statistical methods rather than deep neural networks. The project is organized as a set of smaller packages, each covering a different algorithm or category. The available options include linear regression, logistic regression, support vector machines, decision trees, random forests, K-means clustering, Naive Bayes classifiers, dimensionality reduction methods like PCA and t-SNE, and several others. There are also preprocessing packages for normalizing data and computing nearest neighbors. Each package lists its current status, and most are described as tested or benchmarked. For heavy numerical computation, some packages can optionally use external math libraries, called BLAS and LAPACK backends, instead of the default pure-Rust implementation. Supported backends include OpenBLAS and Intel MKL. The library also supports running in the browser via WebAssembly with an optional feature flag. Linfa is dual-licensed under MIT and Apache 2.0. The project's README acknowledges that building a complete machine learning ecosystem in Rust requires sustained community effort, and it invites contributors to review the public roadmap and get involved.

Copy-paste prompts

Prompt 1

Using linfa, write a Rust program that trains a K-means clustering model on a CSV dataset and prints the cluster assignments.

Prompt 2

How do I add the linfa logistic regression crate to my Rust project and use it to classify text labels?

Prompt 3

Show me how to use linfa-reduction to run PCA on a dataset in Rust and plot the top 2 components.

Prompt 4

Build a decision tree classifier with linfa in Rust that trains on a labeled dataset and reports accuracy on a test split.

Prompt 5

How do I enable the OpenBLAS backend in a linfa project to speed up matrix operations?

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