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. ONNX Runtime supports a wide range of hardware and provides optimizations to improve the performance of model inference.
When using ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 33 : FAIL : Invalid model weights. This error indicates that there is an issue with the model weights during the loading or execution of the model.
The error code 33 signifies a failure related to the model weights. This typically occurs when the weights are not in the expected format or are corrupted. The ONNX Runtime expects the weights to be correctly formatted and intact for successful model inference.
Some common causes of this error include:
First, ensure that the model file is not corrupted. You can do this by checking the file size and comparing it with the expected size. Additionally, try re-downloading the model from a trusted source if possible.
Ensure that the model is compatible with the version of ONNX Runtime you are using. You can check the compatibility by referring to the ONNX Runtime documentation and verifying the supported model opset versions.
If the model was serialized incorrectly, try re-serializing it using a reliable library or tool. Ensure that the serialization process preserves the integrity of the model weights.
Consider updating to the latest version of ONNX Runtime, as newer versions may have bug fixes and improvements that resolve compatibility issues. You can update ONNX Runtime using pip:
pip install --upgrade onnxruntime
By following these steps, you should be able to resolve the "Invalid model weights" error in ONNX Runtime. Ensuring model integrity and compatibility with the runtime version are crucial steps in troubleshooting this issue. For more detailed guidance, refer to the official ONNX Runtime documentation.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
