End-to-End AI for NVIDIA-Based PCs: CUDA and TensorRT Execution Providers in ONNX Runtime

This post is the fourth in a series about optimizing end-to-end AI. As explained in the previous post in the End-to-End AI for NVIDIA-Based PCs series…

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NVIDIA
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Maximilian Müller
•8 min read•advanced•
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Overview

The article discusses the use of CUDA and TensorRT execution providers in ONNX Runtime for optimizing AI applications on NVIDIA-based PCs. It highlights the differences between the two execution providers, their deployment considerations, and provides a sample application demonstrating their capabilities.

What You'll Learn

1

How to deploy ONNX models using CUDA and TensorRT execution providers

2

Why to choose between CUDA EP and TensorRT EP based on application needs

3

How to optimize inference performance with FP16 and FP8 precision

4

When to use CUDA graphs to reduce CPU overhead in neural network inference

Prerequisites & Requirements

  • Understanding of ONNX and neural network inference
  • Familiarity with NVIDIA CUDA and TensorRT libraries

Key Questions Answered

What are the differences between CUDA EP and TensorRT EP in ONNX Runtime?
CUDA EP uses cuDNN for granular operation blocks and performs an exhaustive search for optimal kernels, while TensorRT EP optimizes the entire graph and selects the best execution path, leading to potentially faster inference but longer engine creation times.
How can I optimize inference performance using TensorRT?
To optimize inference performance with TensorRT, enable FP16 and FP8 precision during session creation and consider the workspace size for intermediate results. This allows TensorRT to rearrange operations for better efficiency.
What deployment considerations should I keep in mind for TensorRT?
When deploying TensorRT, it is advisable to ship the generated engine along with the ONNX file to avoid building the model on the user's hardware. This can save time and improve user experience during the first inference.
When should I use CUDA graphs in my application?
CUDA graphs should be used when processing multiple frames in video workloads, as they reduce CPU launch overhead after the initial capture, leading to improved performance for subsequent inferences.

Technologies & Tools

Backend
Cuda
Used for GPU acceleration in ONNX Runtime.
Backend
Tensorrt
Used for optimizing inference performance in ONNX Runtime.
Library
Cudnn
Provides optimized routines for deep neural networks in CUDA EP.

Key Actionable Insights

1
Utilize TensorRT for optimizing the entire inference graph to achieve faster execution times.
This is particularly beneficial for complex models where operation reordering can significantly enhance performance.
2
Consider caching generated TensorRT engines to improve deployment efficiency.
By caching engines specific to the ONNX file and GPU architecture, you can reduce the time required for model initialization during application startup.
3
Experiment with FP16 and FP8 precision settings to maximize performance on NVIDIA GPUs.
These precision settings can lead to substantial performance gains, especially in deep learning applications where speed is critical.

Common Pitfalls

1
Neglecting to cache TensorRT engines can lead to longer initialization times on first inference.
Without caching, users will experience delays as the engine is built on their hardware during the first run, which can be avoided by pre-generating and shipping the engine.
2
Using the wrong precision settings can negatively impact performance.
Not enabling FP16 or FP8 can result in slower inference times, especially on NVIDIA GPUs designed to leverage these precisions.

Related Concepts

Onnx Runtime
Nvidia Ada Lovelace Architecture
AI Model Optimization