Deployment

Deploy MATLAB^® algorithms as a standalone executable on the hardware

The GPU Coder™ Support Package for NVIDIA^® GPUs enables you to deploy your MATLAB function on the hardware. The function is deployed as a standalone executable that continues to run even if the hardware live connection is disconnected from the host computer.

Functions

expand all

Board Connection

`jetson`	Create connection to NVIDIA Jetson hardware
`drive`	Create connection to NVIDIA DRIVE hardware

Web Camera

`webcam`	Create webcam object to acquire images from a webcam attached to the NVIDIA DRIVE or Jetson hardware
`snapshot`	Capture RGB image from Camera
`image`	Display image
`imageDisplay`	Create imageDisplay object for displaying images on NVIDIA DRIVE or Jetson targets
`updatePeripheralInfo`	Scan for and update the list of peripherals connected to the target hardware

File Operations

`getFile`	Transfer file from NVIDIA hardware to host computer
`putFile`	Transfer file from host computer to target hardware
`deleteFile`	Delete file on target hardware

Utilities

`openShell`	Open terminal on host computer to use Linux shell on NVIDIA hardware
`system`	Run command in Linux shell on NVIDIA hardware
`getL4TVersion`	Get the L4T version of the NVIDIA Jetson hardware
`getPdkorSdkVersion`	Get the current DriveWorks SDK installed on the DRIVE hardware
`getDisplayEnvironment`	Get the display environment value used for redirecting the display on the target
`setDisplayEnvironment`	Set the display environment value used for redirecting the display on the target
`setupCodegenContext`	Select the target hardware to build code for from multiple live connection objects
`getLinuxVersion`	Get information about the Linux environment on the target
`killApplication`	Kill an application on the NVIDIA target by name
`killProcess`	Kill a process on the NVIDIA target by ID
`runApplication`	Launch an application on the NVIDIA target by name
`runExecutable`	Launch an executable on the NVIDIA target by name

Processor-in-the-Loop

`setPILTimeout`	Set the timeout value that PIL uses for reading data
`setPILPort`	Set the TCP/IP port number used by the PIL execution
`getPILTimeout`	Get the timeout value that PIL uses for reading data
`getPILPort`	Get the TCP/IP port number used by the PIL execution

Objects

`jetson`	Connection to NVIDIA Jetson hardware
`drive`	Connection to NVIDIA DRIVE hardware
`webcam`	Connection to web camera
`imageDisplay`	NVIDIA display object

Topics

Build and Run an Executable on NVIDIA Hardware

Build and run an executable on NVIDIA hardware.

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

Use GPU Coder app to build and run an executable on NVIDIA hardware.

Read Video Files on NVIDIA Hardware

Generate CUDA code for reading video files on the NVIDIA target by using the videoReader function.

Stop or Restart an Executable Running on NVIDIA Hardware

Stop or restart an executable running on the hardware.

Processor-In-The-Loop Execution from Command Line

Use PIL execution to verify the numerical behavior of the generated code at the MATLAB command line.

Processor-In-The-Loop Execution with the GPU Coder App

Use the GPU Coder app to verify the numerical behavior of the generated code.

Execution-Time Profiling for PIL

Why measure execution times for code generated from entry-point functions.

Featured Examples

Deploy and Run Sobel Edge Detection with I/O on NVIDIA Jetson

Deploy Sobel edge detection that uses webcam and display on the NVIDIA® Hardware using the GPU Coder™ Support Package for NVIDIA® GPUs. In the example Sobel Edge Detection using Webcam on NVIDIA Hardware, we have seen accessing the webcam on NVIDIA hardware using the GPU Coder Support Package for NVIDIA GPUs. In the current example, we will generate code for accessing I/O (webcam and display) to deploy on the NVIDIA hardware. We will consider the same Sobel edge algorithm for showing this capability.

Open Script

Sobel Edge Detection using Webcam on NVIDIA Jetson

Capture and process images from a webcam connected to the NVIDIA® Jetson using the GPU Coder™ Support Package for NVIDIA GPUs. The GPU Coder Support Package for NVIDIA GPUs allows you to capture images from the web camera 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.

Open Script

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.

Open Script

Processor-in-the-Loop Execution on NVIDIA Targets using GPU Coder

How GPU Coder™ Support Package for NVIDIA® GPUs enables the GPU Coder product to run PIL execution on NVIDIA® DRIVE™ and Jetson hardware platforms. A fog rectification example is used to demonstrate this feature. For more information on fog rectification function, see GPU Code generation for fog rectification.

Open Script

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.

Open Script

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.

Open Script

Deploy and Run Fog Rectification for Video on NVIDIA Jetson

Generate and deploy a CUDA® executable for a video-based fog rectification application. It demonstrates the deployable code generation capabilities that the GPU Coder™ Support Package for NVIDIA® GPU provides for the MATLAB® VideoReader function. This example generates a CUDA application that reads the contents of a video file, performs fog rectification operation, and displays the output video on the NVIDIA hardware.

Open Script