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 various platforms and devices. By providing a consistent API, ONNX Runtime allows developers to run models trained in different frameworks like PyTorch, TensorFlow, and others, ensuring interoperability and flexibility.
When using ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 34 : FAIL : Model inference failed. This error indicates that the model inference process has failed, preventing the model from producing predictions or outputs as expected.
Developers may notice that the model does not return any results or outputs, and the error message is logged in the console or application logs. This can be particularly frustrating when deploying models in production environments.
The error code 34 in ONNX Runtime typically signifies a failure during the model inference phase. This can occur due to several reasons, such as incorrect model inputs, incompatible model configurations, or issues within the model itself.
To address the ONNXRuntimeError: [ONNXRuntimeError] : 34 : FAIL : Model inference failed, follow these steps:
Ensure that the input data being fed into the model matches the expected input shape and data type. You can check the model's input requirements using the ONNX model's metadata. Use the following Python code snippet to inspect the model's input details:
import onnx
model = onnx.load('your_model.onnx')
for input in model.graph.input:
print(input.name, input.type)
Ensure that the model is correctly configured for inference. This includes verifying that the model file is not corrupted and is compatible with the version of ONNX Runtime you are using. You can validate the model using the ONNX checker:
import onnx
onnx.checker.check_model('your_model.onnx')
Ensure you are using the latest version of ONNX Runtime, as updates often include bug fixes and performance improvements. You can update ONNX Runtime using pip:
pip install --upgrade onnxruntime
For more information on troubleshooting ONNX Runtime errors, refer to the official ONNX Runtime documentation. Additionally, the ONNX Runtime GitHub Issues page can be a valuable resource for finding solutions to common problems.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
