Why this course?

Description

 enhance inference performance on NVIDIA GPUs and edge devices such as Jetson Nano using TensorRT. This process is essential for deploying efficient, low-latency AI applications in real-time environments.

In modern AI-driven applications—particularly in real-time edge computing such as autonomous vehicles, surveillance systems, and robotics—model performance and inference speed are critical. While PyTorch is widely used for research and training purposes, its deployment capabilities can be significantly enhanced through NVIDIA’s TensorRT, a high-performance deep learning inference optimizer and runtime library.

Key Concepts Covered:

• Introduction to TensorRT and its role in inference optimization.
• Exporting a trained PyTorch model to ONNX (Open Neural Network Exchange) format.
• Converting ONNX models to TensorRT engines.
• Practical example: Converting a CNN model for object detection or classification.
• Deployment on NVIDIA Jetson Nano or Xavier using TensorRT runtime.
• Benchmarking TensorRT performance vs. native PyTorch inference.

Learning Objectives:

• By the end of this chapter, learners will be able to:
• Understand the benefits of deploying models using TensorRT over native PyTorch.
• Convert PyTorch-trained models into ONNX format and subsequently into TensorRT engines.
• Deploy optimized inference models on NVIDIA Jetson platforms.
• Measure and compare inference time before and after TensorRT optimization.
• Troubleshoot common compatibility issues during conversion.

Hands-On Exercise:

• Convert a custom-trained CNN model from PyTorch to ONNX.
• Use trtexec or Python-based TensorRT APIs to compile the model.
• Run inference on sample image data and measure performance.
• Deploy on Jetson Nano and compare latency with standard PyTorch inference.

Tools and Libraries:

• PyTorch
• ONNX
• TensorRT SDK
• Jetson SDK / JetPack
• NVIDIA Nsight Systems (optional for profiling)