ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. It is designed to accelerate the deployment of machine learning models across various platforms and devices. By supporting a wide range of hardware accelerators, ONNX Runtime helps developers optimize and run their models efficiently.
When using ONNX Runtime, you might encounter the following error message:
ONNXRuntimeError: [ONNXRuntimeError] : 40 : FAIL : Model optimization not supported
This error indicates that the optimization technique you are trying to apply to your model is not supported by ONNX Runtime.
The error occurs when a model optimization request is made that is not compatible with the current version of ONNX Runtime. This could be due to using an outdated version of ONNX Runtime or attempting an optimization that is not yet implemented.
Model optimizations in ONNX Runtime are techniques used to improve the performance and efficiency of machine learning models. These optimizations can include quantization, pruning, and operator fusion, among others. However, not all optimizations are supported in every version of ONNX Runtime.
First, check the ONNX Runtime documentation to ensure that the optimization you are trying to apply is supported. The documentation provides a comprehensive list of supported optimizations and their compatibility with different versions of ONNX Runtime.
If the optimization is supported in a newer version, update your ONNX Runtime installation. You can do this using pip:
pip install --upgrade onnxruntime
Ensure that your environment is using the latest version by checking the installed version:
python -c "import onnxruntime; print(onnxruntime.__version__)"
If the optimization is not supported, consider adjusting your model or optimization settings. You may need to choose a different optimization technique that is compatible with ONNX Runtime.
By following these steps, you can resolve the "Model optimization not supported" error in ONNX Runtime. Always ensure that you are using a compatible version of ONNX Runtime and refer to the official ONNX Runtime website for the latest updates and support.
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