End-to-End AI for NVIDIA-Based PCs: NVIDIA TensorRT Deployment

This post is the fifth in a series about optimizing end-to-end AI. NVIDIA TensorRT is a solution for speed-of-light inference deployment on NVIDIA hardware.

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NVIDIA
•
Maximilian Müller
•10 min read•intermediate•
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•View Original
PythonRoBERTa

Overview

This article discusses the deployment of NVIDIA TensorRT for AI inference on NVIDIA hardware, focusing on optimizing performance and compatibility. It outlines strategies for generating efficient TensorRT engines from ONNX models and addresses deployment challenges on workstations.

What You'll Learn

1

How to generate a TensorRT engine from an ONNX file using trtexec

2

Why precision settings (INT8, FP16, FP32) impact TensorRT performance

3

How to optimize TensorRT engine deployment for different GPU architectures

Prerequisites & Requirements

  • Understanding of ONNX file format and AI model architectures
  • Familiarity with NVIDIA TensorRT and its APIs

Key Questions Answered

How can I generate a TensorRT engine from an ONNX file?
You can generate a TensorRT engine from an ONNX file using the trtexec tool with the command 'trtexec --onnx="model.onnx" --saveEngine="engine.trt"'. This command creates an engine file that can be deployed with your application.
What factors affect TensorRT performance during inference?
TensorRT performance is heavily influenced by the precision of operations used, such as INT8, FP16, or FP32. Lower precision typically results in faster execution, but it may also affect model quality, so careful consideration is needed.
What are the challenges of deploying TensorRT on workstations?
Deploying TensorRT on workstations poses challenges such as the need to compile engines on the user's device, which can be time-consuming. Additionally, engines are specific to GPU compute capabilities, necessitating multiple precompiled engines for different GPUs.

Key Statistics & Figures

Engine sizes for different models
RoBERTa: 475 MB, Fast Neural Style Transfer: 3 MB, sub-pixel CNN: 0 MB, YoloV4: 125 MB, EfficientNet-Lite4: 25 MB
These sizes illustrate the varying storage requirements for different AI models when deploying TensorRT engines.

Technologies & Tools

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AI Inference Framework
Nvidia Tensorrt
Used for optimizing and deploying AI models on NVIDIA hardware.
Model Format
Onnx
Serves as the input file format for generating TensorRT engines.

Key Actionable Insights

1
Utilize the trtexec tool to quickly evaluate TensorRT performance on your ONNX models.
This tool allows you to generate engine files efficiently, making it easier to deploy optimized models without extensive manual configuration.
2
Consider using a timing cache to significantly reduce engine build times.
By saving inference timings during engine creation, you can avoid redundant timing evaluations, which speeds up the deployment process, especially for complex models.
3
Optimize your model's precision settings to balance performance and quality.
Experimenting with INT8 and FP16 can lead to faster inference times, but ensure that the model's accuracy remains acceptable for your application.

Common Pitfalls

1
Failing to consider the GPU compute capability when shipping TensorRT engines.
Each TensorRT engine is tied to a specific GPU architecture, so neglecting to compile engines for different capabilities can lead to deployment issues on user devices.
2
Overlooking the impact of precision on model performance.
Using lower precision can speed up inference but may degrade model accuracy. It's essential to evaluate the trade-offs for your specific application.

Related Concepts

Nvidia Hardware Optimization
AI Model Deployment Strategies
Performance Tuning For AI Inference