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 a variety of platforms and devices. ONNX Runtime supports a wide range of hardware accelerators and provides a flexible interface for integrating with different machine learning frameworks.
When using ONNX Runtime, you may encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 20 : FAIL : Model optimization failed. This error indicates that there was a failure during the model optimization process, which is crucial for enhancing the model's performance during inference.
The error typically occurs when attempting to optimize a model for execution. It may halt the process, preventing the model from being deployed effectively.
The error code 20 signifies a failure in the model optimization phase. This phase involves transforming the model to improve its execution efficiency, which may include operations like constant folding, operator fusion, and layout transformation. A failure in this step can be due to incompatible optimization settings or unsupported operations in the model.
To address the model optimization failure, follow these steps:
Ensure that the optimization settings are compatible with your model. Review the ONNX Runtime optimization documentation for guidance on supported optimizations.
Confirm that your model is in the correct ONNX format and version. Use the ONNX checker tool to validate the model's format:
import onnx
onnx.checker.check_model('your_model.onnx')
Ensure you are using the latest version of ONNX Runtime, as updates may include bug fixes and support for additional optimizations. You can update it using pip:
pip install --upgrade onnxruntime
If the model includes unsupported operators, consider modifying the model or using a different optimization level. Refer to the ONNX Runtime operator documentation for a list of supported operators.
By following these steps, you should be able to resolve the model optimization failure in ONNX Runtime. Ensuring compatibility between your model and the optimization settings is key to successful deployment. For further assistance, consider reaching out to the ONNX Runtime community for support.
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