Install and Setup Prerequisites for NVIDIA Boards

Target Requirements

Hardware

MATLAB^® Coder™ Support Package for NVIDIA^® Jetson™ and NVIDIA DRIVE^® Platforms supports the following development boards:

NVIDIA DRIVE PX 2
NVIDIA Clara AGX™
NVIDIA Jetson AGX Xavier™
NVIDIA Jetson AGX Orin™
NVIDIA Jetson Orin Nano
NVIDIA Jetson Orin NX (GPIO workflows are not supported)
NVIDIA Jetson Nano™
NVIDIA Jetson TX1
NVIDIA Jetson TX2
NVIDIA Jetson Xavier NX

The support package uses an SSH connection over TCP/IP to execute commands while building and running the generated code on the DRIVE or Jetson platforms. Connect the target platform to the same network as the host computer. Alternatively, use an Ethernet crossover cable to connect the board directly to the host computer.

Note

On the Windows^® platform, open port 18735 in the Windows Firewall settings. This port is required to establish a connection to the MATLAB server running on the embedded platforms.

Software

Use the NVIDIA JetPack (NVIDIA) or the DRIVE Platform (NVIDIA) software to install the OS image, developer tools, and the libraries required for developing applications on the Jetson or DRIVE platforms. You can use the Component Manager in the JetPack or the Drive Platform software to select the components to be installed on the target hardware. For installation instructions, refer to the NVIDIA board documentation. At a minimum, you must install:

CUDA^® toolkit.
cuDNN library.
TensorRT library.
OpenCV library.
GStreamer library (v1.0 or higher) for deployment of the videoReader function.

The MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms has been tested with the following JetPack and DRIVE SDK versions:

Hardware Platform	Software Version
Jetson AGX Orin	JetPack 5.1.1.
Jetson Orin Nano
Jetson AGX Xavier
Jetson Xavier NX
Clara AGX	Holoscan SDK Note TensorRT and GPIO workflows are not supported.
Jetson Nano	JetPack 4.6.1.
Jetson TX1
Jetson TX2
DRIVE	DRIVE SDK 5.0.10.3-12606092

Install the Simple DirectMedia Layer (SDL v1.2) library, V4L2 library, and V4L2 utilities for running the webcam examples. You must also install the development packages for these libraries.
For deploying the Audio File Read Simulink^® block, install the Sound eXchange (SoX) utility and its development and format libraries.
For example, on Ubuntu^®, use the apt-get command to install these libraries.
```
sudo apt-get install libsdl1.2-dev v4l-utils sox libsox-fmt-all libsox-dev
```
For using Modbus^® functions and blocks, install the libmodbus libraries from https://github.com/stephane/libmodbus.
For example, on Ubuntu, use the following commands to install these libraries.
```
git clone https://github.com/stephane/libmodbus
cd libmodbus
./autogen.sh
./configure && sudo make install
```

Environment Variable on the Target

The support package uses environment variables to locate the tools, compilers, and libraries required for code generation. Set the environment variables in the table.

Variable Name	Default Value	Description
`PATH`	`/usr/local/cuda/bin`	Path to the CUDA toolkit executable on the Jetson or DRIVE platform.
`LD_LIBRARY_PATH`	`/usr/local/cuda/lib64`	Path to the CUDA library folder on the Jetson or DRIVE platform.

The required environment variables must be accessible from non-interactive SSH logins. For example, you can use the export command at the beginning of the $HOME/.bashrc shell configuration file to add the environment variables.

Example .bashrc File

Alternatively, you can set system-wide environment variables in the /etc/environment file. You must have sudo privileges to edit this file.

Example /etc/environment File

Input Devices

Camera connected to the USB or CSI port of the target hardware.
USB audio device for recording and playback of audio signals.

Development Host Requirements

MathWorks Products

MATLAB (required).
MATLAB Coder (required).
GPU Coder™ (required for GPU targeting).
Parallel Computing Toolbox™ (required for GPU targeting).
Simulink (required for generating code from Simulink models).
Computer Vision Toolbox™ (recommended).
Deep Learning Toolbox™ (required for deep learning).
Embedded Coder^® (recommended).
Image Processing Toolbox™ (recommended).
Simulink Coder (required for generating code from Simulink models).
MATLAB Coder Interface for Deep Learning and GPU Coder Interface for Deep Learning support packages (required for deep learning).

Third-Party Products

NVIDIA GPU enabled for CUDA.
CUDA toolkit and driver.
C/C++ Compiler.
CUDA Deep Neural Network library (cuDNN).
NVIDIA TensorRT – high performance deep learning inference optimizer and run-time library.

For information on the version numbers for the compiler tools and libraries, see Installing Prerequisite Products (GPU Coder). For information on setting up the environment variables on the host development computer, see Setting Up the Prerequisite Products (GPU Coder).