e-tornike/best-of-ml-rust

🏆 A ranked list of awesome machine learning Rust libraries.

What it solves

Finding the right Rust library for machine learning can be time-consuming because projects vary widely in quality, maintenance, and popularity. This list solves that by gathering and ranking 230 open-source Rust ML projects into one place, so you can quickly spot the strongest options for your use case.

How it works

The list is automatically compiled and scored. Each project’s quality score is computed from metrics pulled from GitHub and package managers (like stars, forks, contributors, issues, downloads, and release freshness). Projects are grouped into 10 clear categories, such as Machine Learning Frameworks, Text Data & NLP, and MLOps. Each entry shows its score, star count, activity status, and other useful signals, with links to the source repo. You can suggest additions or edits by opening an issue, submitting a pull request, or editing the projects.yaml file directly.

Who it’s for

Rust developers who want to build, deploy, or evaluate machine learning systems in Rust. It’s also useful for researchers, hobbyists, or maintainers who want a fast, evidence-based map of the Rust ML ecosystem.

Highlights

  • Curated 230 projects with a total of 510K stars
  • Ranked by an automated quality score combining GitHub and package-manager data
  • Divided into 10 categories covering frameworks, NLP, images, data loading, graphs, MLOps, and more
  • Each entry displays stars, contributors, forks, issues, downloads, and activity warnings
  • Contributions are encouraged via issues, pull requests, or direct edits to the YAML data file

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