ONNX Runtime is a high-performance inference engine for deploying machine learning models. It is designed to accelerate the deployment of models in production environments by providing a flexible and efficient runtime for models in the ONNX (Open Neural Network Exchange) format. ONNX Runtime supports a wide range of hardware platforms and is optimized for both CPU and GPU execution.
When working with ONNX Runtime, you may encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 32 : FAIL : Model conversion error. This error indicates that there was a failure during the conversion of a model to the ONNX format, which is a crucial step for utilizing ONNX Runtime.
The error code 32 in ONNX Runtime typically signifies a failure in the model conversion process. This can occur due to several reasons, such as unsupported operations, version mismatches, or incorrect model configurations. Understanding the specific cause requires examining the conversion logs and ensuring that all dependencies are correctly set up.
To resolve the model conversion error, follow these steps:
Ensure that the model you are trying to convert is compatible with the ONNX version you are using. You can check the supported operators and versions on the ONNX Operators Documentation.
Make sure you are using the latest version of the conversion tools. For example, if you are converting a PyTorch model, ensure that the torch.onnx.export function is up to date. You can update PyTorch using:
pip install torch --upgrade
Review your conversion script for any errors or unsupported configurations. Ensure that all necessary parameters are correctly set and that the script aligns with the model's architecture.
Examine the conversion logs for detailed error messages. These logs can provide insights into which part of the model or conversion process is failing. Adjust the model or script based on these insights.
For more information on troubleshooting ONNX Runtime errors, consider visiting the following resources:
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
