ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 34 : FAIL : Model inference failed

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 34 : FAIL : Model inference failed

Understanding ONNX Runtime

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.

Identifying the Symptom

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.

Common Observations

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.

Exploring the Issue

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.

Potential Causes

Mismatch between the input data shape and the model's expected input shape. Incorrect data types being fed into the model. Model file corruption or incompatibility with the ONNX Runtime version.

Steps to Resolve the Issue

To address the ONNXRuntimeError: [ONNXRuntimeError] : 34 : FAIL : Model inference failed, follow these steps:

1. Verify Model Inputs

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 onnxmodel = onnx.load('your_model.onnx')for input in model.graph.input: print(input.name, input.type)

2. Check Model Configuration

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 onnxonnx.checker.check_model('your_model.onnx')

3. Update ONNX Runtime

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

Additional Resources

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.