Sample Support Guide#
The TensorRT samples demonstrate how to use the TensorRT API for common inference workflows, including model conversion, network building, optimization, and deployment across different platforms.
Note
The TensorRT samples are provided for illustrative purposes only and are not meant to be used or taken as production-quality code examples.
Quick Start#
New to TensorRT? Choose a sample based on your preferred language:
C++ Samples:
“Hello World” for TensorRT from ONNX - Convert an ONNX model to TensorRT and run inference. This is the recommended starting point for C++ users.
Python Samples:
“Hello World” for TensorRT using PyTorch and Python - Build a network from PyTorch and run inference
Run ONNX with TensorRT (Jupyter) - Refactored beginner-friendly sample with interactive notebook
These samples introduce core TensorRT concepts with clear explanations and step-by-step guidance.
In this guide
Sample Explorer — filter samples by difficulty, language, or use case
Building and Running C++ Samples — build and run C++ samples on Linux and Windows
Running Python Samples — install requirements and run Python samples
Cross Compiling Samples — cross-compile samples for QNX, AArch64, and SBSA