TensorRT

Optimize and deploy deep learning models with NVIDIA TensorRT for high-performance inference on NVIDIA GPUs, achieving significant speedups and reduced latency.

intermediate2-4 hours4 steps

The play

Install TensorRT
Download and install TensorRT from the NVIDIA Developer website. Ensure you have a compatible NVIDIA GPU and CUDA toolkit installed. Follow the installation guide specific to your operating system and CUDA version.
Convert a Model to TensorRT
Use the TensorRT API or command-line tools to convert a trained model (e.g., TensorFlow, PyTorch, ONNX) into a TensorRT engine. This involves parsing the model, optimizing the graph, and generating an execution plan.
Load and Run the TensorRT Engine
Load the generated TensorRT engine into your application. Allocate input and output buffers on the GPU, copy input data to the input buffer, execute the engine, and retrieve the results from the output buffer.
Optimize for Performance
Experiment with different TensorRT optimization settings, such as precision (FP16, INT8), dynamic shapes, and layer fusion, to maximize performance for your specific model and hardware. Profile your application to identify bottlenecks and areas for further optimization.

Starter code

Start by installing TensorRT and converting a simple ONNX model using the `trtexec` command-line tool.  Then, load and run the engine in a Python script to verify the setup.

Source

Articledeveloper.nvidia.com