Install and Setup Prerequisites for NVIDIA Boards

Target Requirements

Hardware

GPU Coder™ Support Package for NVIDIA^® GPUs supports the following development platforms:

• NVIDIA Jetson AGX Xavier platform.

• NVIDIA Jetson Nano platform.

• NVIDIA Jetson TX2 embedded platform.

• NVIDIA Jetson TX1 embedded platform.

• NVIDIA DRIVE PX2 platform.

The GPU Coder Support Package for NVIDIA GPUs uses an SSH connection over TCP/IP to execute commands while building and running the generated CUDA^® code on the DRIVE or Jetson platforms. Connect the target platform to the same network as the host computer. Alternatively, you can use an Ethernet crossover cable to connect the board directly to the host computer.

Software

• Use the JetPack or the DriveInstall 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 DriveInstall 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 GPU Coder Support Package for NVIDIA GPUs has been tested with the following JetPack and DRIVE SDK versions:

  Hardware Platform	Software Version
  Jetson AGX Xavier, TX2/TX1	JetPack 3.3
  Jetson Nano,	JetPack 4.1.1
  DRIVE	DRIVE SDK 5.0.3.10

• 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 example, on Ubuntu, use the apt-get command to install these libraries.

  sudo apt-get install libsdl1.2-dev
  sudo apt-get install v4l-utils
  

Environment Variable on the Target

GPU Coder Support Package for NVIDIA GPUs uses environment variables to locate the necessary tools, compilers, and libraries required for code generation. Ensure that the following environment variables are set.

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.

Ensure that the required environment variables are accessible from non-interactive SSH logins. For example, you can use the export command at the beginning of the $HOME/.bashrc shell config file to add the environment variables.

 Example .bashrc File

# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don't do anything
case $- in
    *i*) ;;
      *) 
         export PATH=$PATH:/usr/local/cuda/bin
         export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
         return;;
esac

# don't put duplicate lines or lines starting with space in the history.
# See bash(1) for more options
HISTCONTROL=ignoreboth

# append to the history file, don't overwrite it
shopt -s histappend

# for setting history length see HISTSIZE and HISTFILESIZE in bash(1)
HISTSIZE=1000
HISTFILESIZE=2000
.
.
.

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

PATH="/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
LD_LIBRARY_PATH="/usr/local/cuda/lib64/"

Input Devices

A webcam connected to the USB host port of the target hardware.

Development Host Requirements

This support package requires the base product, GPU Coder. GPU Coder requires the following MathWorks^® and third-party products.

MathWorks Products

• MATLAB^® (required).

• MATLAB Coder™ (required).

• Parallel Computing Toolbox™ (required).

• Deep Learning Toolbox™ (required for deep learning).

• Image Processing Toolbox™ (recommended).

• Embedded Coder^® (recommended).

• Simulink^® (recommended).

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).

Note

If possible, it is recommended to use the same versions of cuDNN and TensorRT libraries on the target board and the host computer.

Related Topics

• Install Support Package for NVIDIA Hardware
• Getting Started with the GPU Coder Support Package for NVIDIA GPUs