A Comprehensive Guide to Using TensorRT Backend for ONNX

When it comes to optimizing the execution of deep learning models, TensorRT provides a powerful backend specifically designed to accelerate ONNX models. In this guide, we’ll walk through the installation process, executable usage, and how to harness the full potential of TensorRT for your ONNX models. Ready to dive in? Let’s get started!

Understanding the TensorRT Backend for ONNX

The TensorRT backend allows you to parse ONNX models for speedy execution on NVIDIA hardware. By leveraging the capabilities of TensorRT, it enables enhanced performance and efficient deployment of trained models.

Prerequisites for Installation

Before jumping into the installation steps, you’ll want to ensure you have a few dependencies ready:

• Protobuf version 3.0.x
• TensorRT 10.4
• TensorRT 10.4 open source libraries

Installation Steps

Follow these steps to install the TensorRT backend for ONNX:

1. Clone the ONNX-TensorRT repository.
2. Navigate to your ONNX-TensorRT directory:

cd onnx-tensorrt

3. Create a build directory:

mkdir build && cd build

4. Run the CMake command to prepare the build:

cmake .. -DTENSORRT_ROOT=path_to_trt

5. Build the project:

make -j

6. Update your library path:

export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH

Performance Optimization with InstanceNormalization

TensorRT backend allows for two implementations of InstanceNormalization, which may perform differently based on your parameters. By default, the native TensorRT implementation will be utilized. However, if you wish to benchmark using the plugin implementation, simply unset the parser flag. Here’s how:

• For C++:

parser->unsetFlag(nvonnxparser::OnnxParserFlag::kNATIVE_INSTANCENORM);

• For Python:

parser.clear_flag(trt.OnnxParserFlag.NATIVE_INSTANCENORM)

Executable Usage: Testing Your Model

To test if your ONNX model can successfully parse and build into a TensorRT engine, TensorRT provides two tools:

• trtexec for C++ users. Basic command:

trtexec --onnx=model.onnx

• polygraphy for Python users. Basic command:

polygraphy run model.onnx --trt

Building and Using the Python Backend

To get started with the ONNX-TensorRT backend in Python, you simply need to install the required packages:

• Install ONNX:

python3 -m pip install onnx==1.16.0

• Install ONNX-TensorRT:

python3 setup.py install

Here’s a sample Python code to run your model:

import onnx
import onnx_tensorrt.backend as backend
import numpy as np

model = onnx.load("pathtomodel.onnx")
engine = backend.prepare(model, device="CUDA:1")
input_data = np.random.random(size=(32, 3, 224, 224)).astype(np.float32)
output_data = engine.run(input_data)[0]
print(output_data)
print(output_data.shape)

Testing Your Setup

After installation, run the ONNX backend tests to ensure everything is set up correctly:

python onnx_backend_test.py OnnxBackendRealModelTest

To run all tests, simply execute:

python onnx_backend_test.py

Troubleshooting Common Issues

If you encounter any issues during the installation or execution, here are a few troubleshooting tips:

• Ensure that all dependencies are installed and correctly specified in your paths.
• Check if CUDA is properly installed and accessible.
• If you receive errors related to unsupported ONNX operators, verify that you are using a compatible version of TensorRT and check the operator support matrix.
• For clarity on recent changes, refer to the changelog.

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Conclusion

Harnessing the power of TensorRT for ONNX enables significant optimization and performance improvements for deep learning applications. With the steps outlined in this guide, you’re now equipped to start leveraging this technology for your projects. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.