ONNX Runtime is a high-performance inference engine for deploying machine learning models. It is designed to accelerate the deployment of models trained in various frameworks, such as PyTorch, TensorFlow, and more, by providing a unified runtime environment. ONNX Runtime supports models in the ONNX (Open Neural Network Exchange) format, which allows for interoperability between different machine learning frameworks.
When using ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 18 : FAIL : Model version mismatch. This error indicates that there is a compatibility issue between the ONNX model version and the ONNX Runtime version you are using.
The error code 18 : FAIL : Model version mismatch occurs when the ONNX model's version is not supported by the current ONNX Runtime version. This can happen if the model was exported using a newer version of the ONNX specification than what the runtime supports. ONNX models have version numbers that correspond to the ONNX specification they adhere to, and these need to match the capabilities of the runtime environment.
Version mismatches typically occur when there is a discrepancy between the ONNX model's opset version and the opset version supported by the ONNX Runtime. Opset versions define the set of operations (ops) that are available in the model, and newer opset versions may introduce new operations not supported by older runtime versions.
The simplest solution is to update your ONNX Runtime to a version that supports the opset version of your model. You can do this by running the following command:
pip install --upgrade onnxruntime
Ensure that the updated version of ONNX Runtime supports the opset version of your model by checking the ONNX Runtime documentation.
If updating ONNX Runtime is not feasible, you can convert your ONNX model to a compatible opset version. This can be done using the ONNX Python API. Here is an example of how to convert a model:
import onnx
from onnx import version_converter
# Load the model
original_model = onnx.load('model.onnx')
# Convert the model to a lower opset version
converted_model = version_converter.convert_version(original_model, target_version=11)
# Save the converted model
onnx.save(converted_model, 'converted_model.onnx')
Replace target_version=11 with the desired opset version supported by your ONNX Runtime.
By following the steps outlined above, you can resolve the Model version mismatch error in ONNX Runtime. Whether you choose to update the runtime or convert your model, ensuring compatibility between your model and runtime is crucial for successful deployment. For further assistance, refer to the ONNX Runtime documentation.
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