How to Speed Up Deep Learning Inference Using TensorRT

Piotr Wojciechowski

Introduction to accelerated creating inference engines using TensorRT and C++ with code samples and tutorial links

NVIDIA

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Piotr Wojciechowski

•22 min read•intermediate•

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•View Original

Deep LearningPythonPyTorchResNetTensorFlow

Overview

This article provides an introduction to TensorRT, a platform for deep learning inference that enhances throughput and reduces latency by deploying deep learning applications on GPUs. It covers the process of importing models, optimizing them, and generating high-performance runtime engines, with a focus on practical examples and code snippets.

What You'll Learn

1

How to deploy a deep learning application onto a GPU using TensorRT

2

How to import an ONNX model into TensorRT and generate a high-performance runtime engine

3

How to optimize inference performance using mixed precision in TensorRT

Prerequisites & Requirements

Installation of TensorRT and a CUDA-capable GPU
Basic understanding of deep learning concepts and model formats like ONNX(optional)

Key Questions Answered

How can TensorRT improve deep learning inference performance?

TensorRT can improve deep learning inference performance by deploying applications on GPUs, achieving up to 40x faster performance compared to CPU-only platforms. It optimizes runtime engines for various environments, ensuring low latency and high throughput, which is crucial for applications like automotive systems and real-time data processing.

What are the steps to import a model into TensorRT?

The steps to import a model into TensorRT include loading the model from a saved file, converting it to a TensorRT network, optimizing it for the target GPU platform, and generating an engine for inference. This process utilizes components like the ONNX parser and Builder to create an optimized engine tailored for specific hardware.

What is the significance of using mixed precision in TensorRT?

Using mixed precision in TensorRT allows for higher performance and reduced memory usage by employing FP16 and INT8 precisions alongside FP32. This approach minimizes the impact on accuracy while enabling larger models to fit in memory and improving data transfer efficiency, which is essential for real-time applications.

Key Statistics & Figures

Performance improvement

up to 40x faster

Compared to CPU-only platforms for deep learning inference

Technologies & Tools

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Framework

Tensorrt

Used for optimizing and deploying deep learning models on GPUs

Model Format

Onnx

Standard format for representing deep learning models, facilitating model transfer between frameworks

Parallel Computing Platform

Cuda

Enables GPU acceleration for deep learning inference

Key Actionable Insights

1
To maximize the performance of deep learning applications, consider deploying them on GPUs using TensorRT, which can significantly reduce inference times.
This is particularly important for applications requiring real-time processing, such as autonomous driving or live video analysis, where latency is critical.

2
Utilize mixed precision computation in TensorRT to enhance throughput without sacrificing accuracy, especially for large models.
This technique is beneficial in scenarios where memory constraints are present, allowing for the deployment of more complex models on available hardware.

Common Pitfalls

1

A common mistake is to overlook the importance of optimizing the TensorRT engine for the specific GPU architecture being used.

Failing to do so can lead to suboptimal performance, as TensorRT is designed to leverage the unique capabilities of different GPU models. Always ensure that the engine is built with the target hardware in mind.

Related Concepts

Deep Learning Inference

Model Optimization Techniques

Performance Profiling In Cuda