Titan AI LogoTitan AI

onnx

19,606
3,797
Python

Project Description

Open standard for machine learning interoperability

onnx: Open standard for machine learning interoperability

Project Title

onnx — Open Neural Network Exchange for AI Model Interoperability

Overview

ONNX (Open Neural Network Exchange) is an open ecosystem that enables AI developers to choose the right tools as their project evolves. It provides an open source format for AI models, both deep learning and traditional ML, defining an extensible computation graph model, as well as definitions of built-in operators and standard data types. ONNX focuses on inferencing capabilities and is widely supported across various frameworks, tools, and hardware.

Key Features

  • Open source format for AI models
  • Extensible computation graph model
  • Definitions of built-in operators and standard data types
  • Focus on inferencing capabilities
  • Widely supported across frameworks, tools, and hardware

Use Cases

  • Enabling interoperability between different AI frameworks and tools
  • Streamlining the path from research to production in AI projects
  • Increasing the speed of innovation in the AI community by allowing developers to choose the right tools for their evolving projects

Advantages

  • Promotes AI model interoperability across different frameworks and tools
  • Facilitates the transition from research to production in AI projects
  • Encourages community involvement and contribution to the project

Limitations / Considerations

  • Currently focuses mainly on inferencing capabilities
  • May require additional support or plugins for certain frameworks or tools

Similar / Related Projects

  • TensorFlow: A popular open-source machine learning framework that also supports model interoperability, but is not as focused on cross-framework compatibility as ONNX.
  • PyTorch: Another widely-used open-source machine learning framework that offers model serialization, but does not emphasize cross-framework interoperability to the same extent as ONNX.

Basic Information


📊 Project Information

  • Project Name: onnx
  • GitHub URL: https://github.com/onnx/onnx
  • Programming Language: Python
  • ⭐ Stars: 19,457
  • 🍴 Forks: 3,783
  • 📅 Created: 2017-09-07
  • 🔄 Last Updated: 2025-08-20

🏷️ Project Topics

Topics: [, ", d, e, e, p, -, l, e, a, r, n, i, n, g, ", ,, , ", d, e, e, p, -, n, e, u, r, a, l, -, n, e, t, w, o, r, k, s, ", ,, , ", d, n, n, ", ,, , ", k, e, r, a, s, ", ,, , ", m, a, c, h, i, n, e, -, l, e, a, r, n, i, n, g, ", ,, , ", m, l, ", ,, , ", n, e, u, r, a, l, -, n, e, t, w, o, r, k, ", ,, , ", o, n, n, x, ", ,, , ", p, y, t, o, r, c, h, ", ,, , ", s, c, i, k, i, t, -, l, e, a, r, n, ", ,, , ", t, e, n, s, o, r, f, l, o, w, ", ]


📚 Documentation

  • [PyPI - Version
  • [CI
  • [CII Best Practices
  • [OpenSSF Scorecard
  • [REUSE compliant

This article is automatically generated by AI based on GitHub project information and README content analysis

Titan AI Explorehttps://www.titanaiexplore.com/projects/onnx-102692863en-USTechnology

Project Information

Created on 9/7/2017
Updated on 9/18/2025