ONNX Runtime is a high-performance inference engine for machine learning models in the ONNX (Open Neural Network Exchange) format. It is designed to accelerate the deployment of machine learning models across various platforms and devices. ONNX Runtime supports a wide range of hardware accelerators and provides a flexible and efficient way to run models trained in different frameworks.
When using ONNX Runtime, you may encounter the error: ONNXRuntimeError: [ONNXRuntimeError] : 35 : FAIL : Invalid model node. This error indicates that there is an issue with one or more nodes in your ONNX model, preventing it from being processed correctly by the runtime.
During model loading or inference, the process fails, and the above error message is displayed. This typically halts any further execution of the model.
The error code 35 signifies a failure due to an invalid model node. In ONNX models, nodes represent operations or transformations applied to data. If a node is not correctly defined, it can cause the entire model to be invalid.
To resolve the Invalid model node error, follow these steps:
Use the ONNX checker to validate your model. Run the following command:
python -c "import onnx; model = onnx.load('your_model.onnx'); onnx.checker.check_model(model)"
This will help identify any structural issues within the model.
Examine the nodes in your model using tools like Netron. This visual tool allows you to inspect each node's attributes and parameters to ensure they are correctly defined.
Ensure you are using the latest version of ONNX Runtime, as newer versions may support additional operations or provide bug fixes. Update it using:
pip install onnxruntime --upgrade
If the issue persists, consider modifying the model to replace unsupported nodes or operations with supported ones. This may involve retraining or converting the model using a different framework or version.
For further assistance, refer to the ONNX Runtime Documentation and the GitHub Issues page for community support and troubleshooting tips.
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