Deployment

Deploy generated code to NVIDIA® Tegra® hardware targets

You can use GPU Coder™ with the GPU Coder Support Package for NVIDIA GPUs to 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 GPU Coder Support Package for NVIDIA GPUs 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.

To install this support package, use the Add-On Explorer in MATLAB. For information on the supported development platforms, see Install and Setup Prerequisites for NVIDIA Boards (GPU Coder Support Package for NVIDIA GPUs).

Functions

packNGoPackage generated code in zip file for relocation
jetsonCreate connection to NVIDIA Jetson hardware
driveCreate connection to NVIDIA DRIVE hardware

Objects

coder.hardwareCreate hardware board configuration object for C/C++ code generation from MATLAB code
jetsonConnection to NVIDIA Jetson hardware
driveConnection to NVIDIA DRIVE hardware

Topics

Build and Run an Executable on NVIDIA Hardware

Targeting embedded NVIDIA boards from the MATLAB command line.

Build and Run an Executable on NVIDIA Hardware Using GPU Coder App

Targeting embedded NVIDIA boards by using the GPU Coder app.

Relocate Generated Code to Another Development Environment

Package generated files into a compressed file that you can relocate and unpack with a standard zip utility.

Featured Examples

Getting Started with the GPU Coder Support Package for NVIDIA GPUs

Getting Started with the GPU Coder Support Package for NVIDIA GPUs

Use the GPU Coder™ Support Package for NVIDIA GPUs and connect to NVIDIA® DRIVE™ and Jetson hardware platforms, perform basic operations, generate CUDA® executable from a MATLAB® function and run the executable on the hardware. A simple vector addition example is used to demonstrate this concept.

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Sobel Edge Detection on NVIDIA Jetson Nano using Raspberry Pi Camera Module V2

Sobel Edge Detection on NVIDIA Jetson Nano using Raspberry Pi Camera Module V2

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

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Semantic Segmentation on NVIDIA DRIVE

Semantic Segmentation on NVIDIA DRIVE

Generate and deploy a CUDA® executable for an image segmentation application that uses deep learning. It uses the GPU Coder™ Support Package for NVIDIA® GPUs 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 on Semantic Segmentation, see Code Generation for Semantic Segmentation Network.

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

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

Deploy Image Processing Toolbox™ algorithms to NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. The imtophat function that performs morphological top-hat filtering on a grayscale image is used as an example to demonstrate this concept. Top-hat filtering computes the morphological opening of the image (using imopen) and then subtracts the result from the original image. The generated CUDA® code uses shared memory to speed up the operations on the GPU.

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Deployment and Classification of Webcam Images on NVIDIA Jetson TX2 Platform

Deployment and Classification of Webcam Images on NVIDIA Jetson TX2 Platform

Generate CUDA® code from a DAGNetwork object and deploy the generated code onto the NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. This example uses the resnet50 deep learning network to classify images from a USB webcam video stream.

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