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 different platforms and environments. By supporting a wide range of hardware and software configurations, ONNX Runtime enables developers to optimize their models for various use cases.
When working with ONNX Runtime, you may encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 6 : INVALID_GRAPH : Invalid model graph. This error indicates that there is an issue with the model graph, which prevents ONNX Runtime from executing the model correctly.
The INVALID_GRAPH error typically arises when the model graph contains errors or unsupported operations. This can occur due to several reasons, such as:
The error code 6 in the ONNXRuntimeError indicates a specific type of issue related to the model graph's validity. This error prevents the model from being loaded and executed by ONNX Runtime.
Use ONNX tools to validate the model graph and identify any issues. The ONNX GitHub repository provides a onnx.checker module that can be used to check the model's validity:
import onnx
# Load the ONNX model
model = onnx.load('model.onnx')
# Check the model
onnx.checker.check_model(model)
If the model is invalid, the checker will raise an error with details about the issue.
Use the Netron tool to visually inspect the model graph. This can help identify structural issues, such as disconnected nodes or cycles, that may not be obvious from the error message alone.
Based on the findings from the validation and inspection steps, make necessary corrections to the model. This may involve:
After making corrections, re-validate the model using the onnx.checker module and test it with ONNX Runtime to ensure the issue is resolved.
By following these steps, you can diagnose and fix the INVALID_GRAPH error in ONNX Runtime. Ensuring that your model graph is valid and free of unsupported operations is crucial for successful model deployment. For more information, refer to the ONNX Runtime documentation.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
