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NVIDIA Jetson Orin Nano Setup Guide

This guide walks you through how to set up the NVIDIA Jetson Orin Nano from start to finish. You can also find the official Getting Started Guide provided by NVIDIA for setting up the Jetson Orin. This guide builds on top of the official documentation with a focus on adding support for deploying the EdgeFirst pipeline.

Device Specification

The device used in these examples has these specs.

  • 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores
  • 6-core Arm Cortex-A78AE v8.2 64-bit CPU
  • 8GB 128-bit LPDDR5
  • microSD Card slot

I/O:

  • Display Port
  • 4x USB 3.2 Gen2 Type A
  • USB Type-C UFP
  • M.2 Key M 2280
  • M.2 Key M 2230
  • M.2 Key E (populated)

Flash a microSD card with Jetpack 6.x

It is recommended to use a microSD card that's 64GB and higher to flash Jetpack 6.2.1 which is ~24.1GB.

Official Docs

The official documentation links towards these instructions for flashing Jetpack 6.x.

  1. Download Jetpack v6.2.1 by clicking the link provided. This should download a ZIP file named "jetson-orin-nano-devkit-super-SD-image_JP6.2.1.zip"

  2. Use balenaEtcher to flash the SD card using an SD card reader connected to your PC. Select the ZIP file that was downloaded and select the SD card for the storage. Once selected, click "Flash!" to start

    balenaEtcher
    balenaEtcher

  3. Wait for the application to complete flashing ~10 mins. depending on your machine

    balenaEtcher
    balenaEtcher

  4. Once completed, a complete status should appear. Proceed to the next steps to boot the board with the image flashed

    Completed Flashing
    Completed Flashing

Boot the Jetson Orin with Jetpack 6.x

  1. Insert the microSD card into the module

    Insert Micro SD Card
    Insert Micro SD Card

  2. Power on the Jetson by inserting the 19V power supply to the DC Barrel jack (1). Next connect the board to your network by attaching an ethernet cable to the Gigabit Ethernet port (4). For the next steps a mouse and keyboard, and monitor will be needed to setup the system configuration which is connected to the USB3.1 Type A ports (3) and Display Port (2)

    Jetson Orin Physical Connections
    Jetson Orin Physical Connections

  3. Once powered on, the initial software setup (oem-config) will be initiated

Setup System Configurations

Proceed with the setup configuration displayed on the monitor connected to the board. This setup should also allow you to setup your board's username, password, and hostname (“computer’s name”) as shown below. Otherwise, the hostname can be set with the command sudo hostnamectl set-hostname myhostname on the Jetson's terminal.

System Configuration
System Configuration

Set to Maximum Power

Set the board to 25W and prevent any power savings which could affect pipeline performance by running the following commands. 

$ sudo nvpmodel -m 1
$ sudo jetson_clocks

Info

These commands have to be set each time the board boots up as it defaults to run power savings.

  • You can find more information on the various power modes by entering cat /etc/nvpmodel.conf or visiting the page Set the Power Mode
  • You can see the current power mode set with sudo nvpmodel -q

Setup Python

To run the examples for the model validation and model deployments in this Quick Start, certain python dependencies are required.

Install Python's pip and Virtual Environment

These instructions installs Python's pip and venv which does not come pre-installed in the BSP.

  1. Install Python pip sudo apt install python3-pip -y
  2. Install Python virtual environment sudo apt install python3.10-venv
  3. Create a python virtual environment python3 -m venv path/to/myenv --system-site-packages
  4. Activate the virtual environment source path/to/myenv/bin/activate

Install PyCuda and ONNXRuntime

These instructions installs PyCuda and ONNXRuntime which does not come pre-installed in the BSP. The following steps were taken from this Setup Guide.

  1. Set CUDA_HOME environment variable export CUDA_HOME=/usr/local/cuda
  2. Add the CUDA compiler to the PATH export PATH=$CUDA_HOME/bin:$PATH
  3. Add the CUDA libraries export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH

  4. Verify that the CUDA compiler driver is installed in the device.

    $ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Wed_Aug_14_10:14:07_PDT_2024
Cuda compilation tools, release 12.6, V12.6.68
Build cuda_12.6.r12.6/compiler.34714021_0
    5. Install PyCUDA which is needed to run TensorRT models pip install pycuda 6. Install ONNXRuntime depending on your CUDA version which is needed to run ONNX models.

    • CUDA 12.9 pip3 install onnxruntime-gpu --index-url https://pypi.jetson-ai-lab.io/jp6/cu129 'numpy>1.24,<2'
    • CUDA 12.6 pip3 install onnxruntime-gpu --index-url https://pypi.jetson-ai-lab.io/jp6/cu126 'numpy>1.24,<2'

Specific NumPy Version

  • NumPy ≥ 1.24 is required since earlier versions causes TypeError: 'numpy._DTypeMeta' object is not subscriptable upon import pycuda
  • NumPy < 2 is also required since certain modules in the Jetson packages is compiled with NumPy < 2

Next Steps

Now that you have set up your Jetson Orin, you can begin training your Vision model for deployment on this platform. First, copy one of our ready-to-use datasets to use for training.