# ONNX Optimizer [](https://pypi.python.org/pypi/onnxoptimizer/) [](https://pypi.python.org/pypi/onnxoptimizer/) [](https://github.com/onnx/optimizer/pulls) ## Introduction ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes. The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call. You may be interested in invoking the provided passes, or in implementing new ones (or both). ## Installation You can install onnxoptimizer from PyPI: ```bash pip3 install onnxoptimizer ``` Note that you may need to upgrade your pip first if you have trouble: ```bash pip3 install -U pip ``` If you want to build from source: ```bash git clone --recursive https://github.com/onnx/optimizer onnxoptimizer cd onnxoptimizer pip3 install -e . ``` Note that you need to install protobuf before building from source. ## Roadmap * Command-line API (e.g. `python3 -m onnxoptimizer model.onnx output.onnx`) * More built-in pass * Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details) ## Relevant tools * [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer * [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box ## Code of Conduct [ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)