
onnx 1.20.1
Open Neural Network Exchange
Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).
Stars: 20349, Watchers: 20349, Forks: 3871, Open Issues: 285The onnx/onnx repo was created 8 years ago and the last code push was 5 hours ago.
The project is extremely popular with a mindblowing 20349 github stars!
How to Install onnx
You can install onnx using pip
pip install onnx
or add it to a project with poetry
poetry add onnx
Package Details
- Author
- None
- License
- None
- Homepage
- None
- PyPi:
- https://pypi.org/project/onnx/
- GitHub Repo:
- https://github.com/onnx/onnx
Classifiers
Related Packages
Errors
A list of common onnx errors.
Code Examples
Here are some onnx code examples and snippets.
GitHub Issues
The onnx package has 285 open issues on GitHub
- WIP: Fix pr_checks
- Error: Resource not accessible by integration; action suggest-changes does not work
- Fix default attribute values in shape inference
- Build option for OpenSSF Compiler Hardening Flags
- RFC template for large proposals
- [Operator] Grouped matrix multiply
- CMake fails to find ONNX sources on macOS unless onnx_SOURCE_DIR is manually set
- Fix build failure caused by CMake cache pollution
- directly use onnx-weekly at model zoo script
- Fix: prevent int64 overflow when computing tensor element count
- Fix Reshape shape inference to use dim_param when inputProduct is 0
- Clarify DFT behavior when inverse=True and onesided=True (irfft)
- Some operator attribute defaults conflict with protobuf defaults
- Build failure during iterative wheel builds due to CMake cache pollution.
- Python Wheels for the Raspberry Pi (https://www.piwheels.org/project/onnx/)
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