TensorRT

4 Steps

1. 1

   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.

2. 2

   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.

3. 3

   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.

4. 4

   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.