What is ort

The ort project is an unofficial Rust wrapper for ONNX Runtime 1.19, built on the foundation of the now inactive onnxruntime-rs. It leverages ONNX Runtime to accelerate machine learning inference and training on both CPU and GPU. This wrapper provides a robust interface for integrating ML capabilities into Rust applications, making it a valuable tool for developers looking to enhance their projects with efficient ML operations. With extensive documentation, active support channels, and adoption by several notable projects like Twitter, Bloop, and Supabase, ort is definitely worth exploring for anyone interested in integrating ML into their Rust projects.

Project Overview

Key Features

ort is an unofficial Rust wrapper for ONNX Runtime 1.19, built on the now inactive onnxruntime-rs. Here are its main capabilities:

• Acceleration: Accelerates machine learning (ML) inference and training on both CPU and GPU.
• Documentation: Includes a guide, API reference, examples, and migration instructions from v1.x to v2.0.
• Support: Offers support through Discord, GitHub Discussions, and email.
• Use Cases: Used by various projects such as Twitter for homepage recommendations, Bloop for semantic code search, edge-transformers for accelerated transformer model inference, and others like Supabase, Lantern, Magika, and sbv2-api.
• Sponsorship: Allows for sponsorship to support the project.

This wrapper provides a robust and efficient way to integrate ONNX Runtime into Rust applications.

Real-World Applications

Accelerated ML Inference

• Twitter: Uses ort to serve homepage recommendations to hundreds of millions of users, leveraging ONNX Runtime’s acceleration on both CPU and GPU.
• edge-transformers: Utilizes ort for accelerated transformer model inference at the edge, enhancing performance in real-time applications.

Semantic Code Search

• Bloop: Employs ort to power their semantic code search feature, providing faster and more accurate results.

Edge Functions

• Supabase: Uses ort to remove cold starts for their edge functions, ensuring quicker response times.

Database Integration

• Lantern: Integrates ort to provide embedding model inference inside Postgres, enhancing database query performance.

Content Type Detection

• Magika: Uses ort for content type detection, benefiting from the accelerated ML capabilities.

Exploring and Benefiting from the Repository

• Documentation: Refer to the guide, API reference, examples, and migration guide to get started.
• Support: Join Discord discussions, GitHub Discussions, or contact via email for support.
• Contribute: Open a PR to add your project to the list of projects using ort.
• Sponsor: Consider sponsoring ort to support its development and maintenance.

Conclusion

• Accelerates ML Inference and Training: ort wraps ONNX Runtime 1.19 for Rust, enhancing machine learning inference and training on both CPU and GPU.
• Widespread Adoption: Used by major projects like Twitter, Bloop, edge-transformers, Ortex, Supabase, Lantern, Magika, and sbv2-api.
• Community Support: Active support through Discord, GitHub Discussions, and email.
• Future Potential: Continued development and sponsorship could further expand its use in various ML applications.

Key Points

• Enhances ML performance
• Widely adopted by significant projects
• Strong community support
• Open to future growth and sponsorship

For further insights and to explore the project further, check out the original pykeio/ort repository.

Attributions

Content derived from the pykeio/ort repository on GitHub. Original materials are licensed under their respective terms.