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 hardware. By supporting multiple execution providers, such as CPU, GPU, and specialized hardware like TensorRT, ONNX Runtime optimizes model performance and scalability.
When using ONNX Runtime with TensorRT as the execution provider, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 14 : FAIL : TensorRT error. This error indicates a failure during the execution of the model with TensorRT optimizations.
The error typically arises due to compatibility issues between the ONNX model and TensorRT, or incorrect installation/configuration of TensorRT. TensorRT is a library developed by NVIDIA to optimize deep learning models for inference on NVIDIA GPUs. It requires specific versions and configurations to work seamlessly with ONNX models.
To resolve the TensorRT error in ONNX Runtime, follow these steps:
Ensure that TensorRT is correctly installed on your system. You can verify the installation by running:
dpkg -l | grep tensorrt
For more details on installation, refer to the TensorRT Installation Guide.
Ensure that your ONNX model is compatible with TensorRT. Check the opset version and ensure it is supported by the version of TensorRT you are using. You can inspect the opset version using:
import onnx
model = onnx.load('model.onnx')
print(model.opset_import)
Refer to the ONNX-TensorRT GitHub repository for supported opset versions and layers.
If the installation is incorrect or outdated, consider updating or reinstalling TensorRT. Follow the official NVIDIA TensorRT page for the latest version and installation instructions.
If certain layers or operations are unsupported, you may need to modify the ONNX model to replace or remove these layers. Tools like ONNX and ONNX Simplifier can help in optimizing and simplifying the model.
By following these steps, you should be able to resolve the TensorRT error in ONNX Runtime. Ensuring compatibility between your ONNX model and TensorRT, along with a correct installation, is crucial for successful model execution. For further assistance, consider reaching out to the ONNX Runtime GitHub Issues page.
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