Deployment

Deploy generated code toNVIDIA^®Tegra^®硬件目标

You can use GPU Coder™ with theMATLAB^®Coder™ Support Package for NVIDIA Jetson^®and NVIDIA DRIVE^®Platformsto deploy your MATLAB algorithms on embedded NVIDIA GPUs. Specifically, you can target the NVIDIA Jetson and DRIVE family of boards on either Windows^®or Linux^®systems. The support package enables you to remotely communicate with the NVIDIA target and control the peripheral devices for prototyping. The MATLAB entry-point function is deployed as a standalone executable that continues to run even if the hardware live connection is disconnected from the host computer.

要安装此支持软件包，请在MATL金宝appAB中使用附加探索器。有关受支持开发平台的信息，请参见金宝appInstall and Setup Prerequisites for NVIDIA Boards(MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms)。

Note

从R2021a开始，GPU Coder Support Package for NVIDIA GPUs被称为MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms。要在R2021A中使用此金宝app支持包，您必须拥有MATLAB Coderproduct.

Functions

`packNGo`	Package generated code in ZIP file for relocation
`代码gen`	从中生成C/C ++代码MATLAB代码
`jetson`	Create connection toNVIDIAJetsonhardware
`drive`	Create connection toNVIDIA DRIVEhardware

Objects

`代码r.hardware`	Create hardware board configuration object for C/C++ code generation fromMATLAB代码
`jetson`	连接到NVIDIAJetsonhardware
`drive`	连接到NVIDIA DRIVEhardware

Topics

MATLAB

构建和运行一个前女友ecutable on NVIDIA Hardware

Targeting embedded NVIDIA boards from the MATLAB command line.

构建和运行一个前女友ecutable on NVIDIA Hardware Using GPU Coder App

Targeting embedded NVIDIA boards by using the GPU Coder app.

将生成的代码搬迁到另一个开发环境

包装生成的文件中的压缩文件可以使用标准ZIP实用程序重新安置和打开包装。

金宝app

靶向NVIDIA嵌入式板

构建并部署到NVIDIA GPU板。

数值等效测试

Compare results from model and generated code simulations.

Parameter Tuning and Signal Monitoring by Using External Mode

Tune parameters and monitor signals through a TCP/IP communication channel between development computer and target hardware.

从simulink生成cuda ros节点金宝app（ROS工具箱）

配置Simulink金宝app®Coder™以生成并从Simulink模型生成和构建CUDA®ROS节点。

Lane and Vehicle Detection in ROS Using YOLO v2 Deep Learning Algorithm（ROS工具箱）

此示例显示了如何在ROS启用Simulink®模型内使用深层卷积神经网络来执行车道和车辆检测。金宝app

使用yolov2检测算法的机器人在simulink中使用yolov2检测算法的签名金宝app（ROS工具箱）

此示例显示了如何使用Simulink®控制在单独的基于ROS金宝app的模拟器上运行的模拟机器人。

Featured Examples

NVIDIA JETSON和NVIDIA驱动器平台的MATLAB编码器支持包金宝app入门

Use theMATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms带有来自NVIDIA®的嵌入式板。该示例使用简单的矢量添加算法来说明：从MATLAB环境中连接到嵌入式板的连接。

打开脚本

使用Raspberry Pi摄像头V2在Nvidia Jetson Nano上进行SOBEL边缘检测

Capture and process images from a Raspberry Pi Camera Module V2 connected to the NVIDIA® Jetson Nano. The MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms allows you to capture images from the Camera Module V2 and bring them into the MATLAB environment for processing. In this example you learn how to develop a Sobel edge detection algorithm by using this capability.

打开脚本

NVIDIA DRIVE上的语义细分

Generate and deploy a CUDA® executable for an image segmentation application that uses deep learning. It uses the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms to deploy the executable on the NVIDIA DRIVE™ platform. This example performs code generation on the host computer and builds the generated code on the target platform by using remote build capability of the support package. For more information, see Code Generation for Semantic Segmentation Network.

打开脚本

Top-Hat Filtering to Remove Uneven Background Illumination on NVIDIA Jetson TX2 Developer Kit

使用NVIDIA GPU的GPU CODER™支持软件包将图像处理工具箱™算法部署到NVIDIA®JETSONTX2板上。金宝app这imtophat（图像处理工具箱）在灰度图像上执行形态学上帽过滤的函数被用作证明此概念的示例。顶帽过滤计算图像的形态开放（使用imopen（图像处理工具箱）），然后从原始图像中减去结果。生成的CUDA®代码使用共享内存来加快GPU上的操作。

打开脚本

Deployment and Classification of Webcam Images on NVIDIA Jetson TX2 Platform

使用dagnetwork对象生成CUDA®代码，并使用NVIDIA GPU的GPU Coder™支持软件包将生成的代码部署到NVIDIA®Jetson®TX2板上。金宝app此示例使用RESNET50深度学习网络从USB网络摄像头视频流进行分类。

打开实时脚本

Simulink上的NVIDIA JETSON TX2平台上部署和分类网络摄像头图像金宝app

Deploy a Simulink® model on the NVIDIA® Jetson TX2 board for classifying webcam images. This example classifies images from a webcam in real-time by using the pretrained deep convolutional neural network,Resnet-50。该示例中金宝app的Simulink模型使用摄像头和显示块中的Nvidia Jetson和NVIDIA驱动器平台的MATLAB®CODER™支持软件包从网络摄像头捕获实时视频流，并在连接金宝app到Jetson平台的监视器上显示预测结果。