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 a variety of platforms and devices. ONNX Runtime supports a wide range of operators and is optimized for both CPU and GPU execution, making it a versatile choice for developers looking to integrate machine learning into their applications.
When using ONNX Runtime, you might encounter the following error message:
ONNXRuntimeError: [ONNXRuntimeError] : 4 : NOT_IMPLEMENTED : This feature is not implemented
This error indicates that a specific operation or feature you are trying to use is not supported by the current version of ONNX Runtime.
The error code NOT_IMPLEMENTED suggests that the operation or feature you are attempting to use is not available in the version of ONNX Runtime you are currently using. This can occur if the model you are trying to run includes operators or features that have not yet been implemented in ONNX Runtime.
To resolve this issue, follow these steps:
First, verify whether the feature or operator you are trying to use is supported by ONNX Runtime. You can find the list of supported operators in the ONNX Runtime Operator Documentation.
If the feature is supported in a newer version, update your ONNX Runtime installation. You can update ONNX Runtime using pip:
pip install --upgrade onnxruntime
For GPU support, use:
pip install --upgrade onnxruntime-gpu
If updating ONNX Runtime does not resolve the issue, consider modifying your model to use supported operators. You can use tools like ONNX to convert or optimize your model.
By following these steps, you should be able to resolve the "NOT_IMPLEMENTED" error in ONNX Runtime. Keeping your ONNX Runtime version up to date and ensuring your models use supported features will help prevent such issues in the future.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
