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 machine learning model deployment across various platforms and devices, ensuring efficient execution of models trained in different frameworks.
For more information about ONNX Runtime, visit the official ONNX Runtime website.
When working with ONNX Runtime, you might encounter the following error message:
ONNXRuntimeError: [ONNXRuntimeError] : 27 : FAIL : Invalid model metadata
This error indicates that there is an issue with the metadata associated with your ONNX model.
Model metadata in ONNX provides essential information about the model, such as its name, version, author, and other relevant details. This metadata is crucial for ensuring that the model can be correctly interpreted and executed by ONNX Runtime.
The error typically arises when the model's metadata is either incomplete or incorrectly formatted. This can happen if the metadata fields are missing or contain invalid values.
First, ensure that all required metadata fields are present in your ONNX model. These fields typically include:
Use a tool like ONNX's Python API to inspect and modify the model's metadata if necessary.
Ensure that the metadata fields are correctly formatted. For example, the version should be a valid number, and the name should be a string. Use a JSON validator to check the format if the metadata is stored in JSON format.
If you identify any issues with the metadata, update the model using a script or tool that can modify ONNX files. For instance, you can use Python to update the metadata:
import onnx
# Load the model
model = onnx.load('model.onnx')
# Update metadata
model.metadata_props.append(onnx.StringStringEntryProto(key='author', value='Your Name'))
# Save the updated model
onnx.save(model, 'updated_model.onnx')
By ensuring that your ONNX model's metadata is complete and correctly formatted, you can resolve the "Invalid model metadata" error in ONNX Runtime. This will allow you to successfully deploy and execute your machine learning models.
For further reading, consider checking the ONNX Metadata Documentation.
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