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
- GitHub: https://github.com/onnx/onnx
- Stars: 19,457
- License: Apache-2.0
- Last Commit: 2025-08-20
📊 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, ", ]
🔗 Related Resource Links
📚 Documentation
- Overview
- ONNX intermediate representation spec
- Versioning principles of the spec
- Operators documentation
- Python API Overview
- Shape and Type Inference
- Opset Version Conversion
- this document
🌐 Related Websites
- [
- [
- [
- [
- [
This article is automatically generated by AI based on GitHub project information and README content analysis