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Jetson AGX Orin📖
01.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flash new system to the onboard eMMC
  • 03.Flash new system to an NVMe SSD
  • 04.Install Jetpack environment
  • 05.Backup SSD system
  • 06.Restore factory image system
  • 07.SSD expansion
  • 08.Solid State Drive Speed Test
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
  • 10. Change screen resolution
  • 11. Virtual Desktop
  • 12. Set root user password
  • 13. Configure sudo passwordless operation
  • 14. Managing custom services
  • 15. View system version information
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
06.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
06.YOLO26 development
  • 01.YOLO Environment Setupnew
  • 02.CLI Usagenew
  • 03.Object Detectionnew
  • 04.Instance Segmentationnew
  • 05.Pose Estimationnew
  • 06.Image Classificationnew
  • 07.Oriented Bounding Boxes Object Detectionnew
  • 08.Model Conversionnew
  • 09.Dataset Annotationnew
  • 10.Model Training_Conversionnew
  • 11.Model Predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS2-Humble
01.ROS2 basic course
  • 3.ROS2 development environment
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
10.Offline AI model course
Offline large model (Text Interaction)
  • 10.AI large model offline voice assistant
  • 1.Al large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
11.Online AI model course
Online large model (Text Interaction)
  • 8.AI large model offline voice assistant
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
12.Offline Generative AI Applications
  • 01.Children's Storytelling Session
  • 02.Cooking Assistant
  • 03.Homework tutor
  • 04.Image to animation
13.OpenClaw Advanced Development
  • 00.OpenClaw Quick Start on Jetson Platform
  • 01.OpenClaw Installation
  • 02.OpenClaw Online Platform Deployment
  • 03.OpenClaw Local Model Deployment
  • 04.OpenClaw TUI Interaction
  • 05.OpenClaw WebChat Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10. OpenClaw Switching Models
  • 11.OpenClaw Application - File Management
  • 12.OpenClaw Application - Camera
  • 13.OpenClaw Application - Browser Control
  • 14.OpenClaw Application - Script Execution
  • 15.OpenClaw Application - AI Programming
  • 16.OpenClaw Application - Dedicated AI Assistant
  • 17.OpenClaw WhatsApp Plugin Application
Download
  • System_File
  • VM_File
  • Code
  • Tools
Download ZIP

Welcome to Jetson AGX Orin repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

02.Use Jupyter Lab

Use Jupyter Lab

Use Jupyter Lab1. Jupyter Lab installation1.1. Jupyter Lab1.2、Node.js2. Jupyter Lab startup2.1. Set the default browser2.2. Start Jupyter Lab2.3. Host access3. Jupyter Lab configuration3.1. LAN access3.1.1, create a configuration file3.1.2, modify the configuration file3.2, Configure access password3.3, Start the service automatically at boot3.3.1, Edit the service file3.3.2, Set up automatic serviceAutomatic service at startupStart the serviceCheck the service statusVerify auto-startup4. Use Jupyter Lab4.1. Kernel4.2. Run the program4.2.1. Running4.2.2. Running completed

1. Jupyter Lab installation

1.1. Jupyter Lab

Use the following command to install Jupyter Lab: If the download speed of Jupyter Lab is slow, you can use the specified source to install it

sudo apt update
sudo apt install python3-pip -y
sudo pip3 install --upgrade pip
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sudo pip3 install jupyterlab
# Tsinghua source: pip3 install jupyterlab -i https://pypi.tuna.tsinghua.edu.cn/simple
# Alibaba Cloud source: sudo pip3 install jupyterlab -i https://mirrors.aliyun.com/pypi/simple/

image-20241226141045667

image-20241226142651775

1.2、Node.js

Use the following command to install the latest Node.js:

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sudo apt install curl -y
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sudo curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
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sudo apt install nodejs -y

Verify the version:

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node -v && npm -v

image-20241230104859936

2. Jupyter Lab startup

Before starting Jupyter Lab, you need to set the system default browser, otherwise some prompts will appear when starting the terminal.

2.1. Set the default browser

Open the system Chromium browser and select Set the default browser:

image-20241226141850048

image-20241226141855084

2.2. Start Jupyter Lab

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jupyter lab
# Start without browser jupyter lab --no-browser
# Start as administrator sudo jupyter lab --allow-root

image-20241226143316322

image-20241226143326168

2.3. Host access

The host refers to the Jetson motherboard system access, which can be accessed directly through http://localhost:8888/:

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http://localhost:8888/

image-20250113180325457

3. Jupyter Lab configuration

Configure LAN access, access password, and auto-start for Jupyter Lab.

3.1. LAN access

Device in the same LAN can be accessed by entering IP:8888 in the browser!

Note: The LAN of the campus network is generally inaccessible. You can change the laptop/mobile phone hotspot to test

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For example, the motherboard IP: 192.168.2.114; we can enter 192.168.2.114:8888 through the browser in the same LAN to perform Jupyter Lab on the motherboard

3.1.1, create a configuration file

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sudo jupyter lab --generate-config

The location of the automatically generated configuration file: Writing default config to: /root/.jupyter/jupyter_lab_config.py

3.1.2, modify the configuration file

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sudo gedit /root/.jupyter/jupyter_lab_config.py

Modified content: After modification, click Save and close the file

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# Allow requests from any source to access the Jupyter Lab server
c.ServerApp.allow_origin = '*'
# 0.0.0.0 means binding all available network interfaces and allowing access from any address
c.ServerApp.ip = '0.0.0.0'
# Allow Jupyter Lab server to be started as root user
c.ServerApp.allow_root = True
# Modify the default port to avoid conflicts
c.ServerApp.port = 8888

image-20241226144141750

3.2, Configure access password

Enter the command to set the password in the terminal twice, and the input will not be displayed when entering the password!

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sudo jupyter lab password

Automatically generated configuration file location: [JupyterPasswordApp] Wrote hashed password to /root/.jupyter/jupyter_server_config.json

image-20241226144212497

3.3, Start the service automatically at boot

3.3.1, Edit the service file

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sudo gedit /etc/systemd/system/jupyterlab.service

Add content: Click Save and close the file after adding

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[Unit]
Description=jupyterlab
After=network.target
[Service]
Type=simple
ExecStart=/usr/local/bin/jupyter-lab
config=/root/.jupyter/jupyter_lab_config.py --no-browser
User=root
Group=root
WorkingDirectory=/home/jetson/
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target

root: System user name

ExecStart: Command to start Jupyter lab, change to JupyterLab installation path

config: Change to JupyterLab configuration file path

WorkingDirectory: The working directory opened when Jupyter-lab is started, which can be changed by yourself (it is recommended to change to the user directory)

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View Jupyter-lab installation path: which jupyter-lab
Configuration file path: Refer to the path of the configuration file generated above

image-20241226144326742

3.3.2, Set up automatic service

Automatic service at startup
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sudo systemctl enable jupyterlab
# Disable auto-startup systemctl disable jupyterlab
Start the service
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sudo systemctl start jupyterlab
# Stop the service sudo systemctl stop jupyterlab
Check the service status
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systemctl status jupyterlab

image-20241226144733028

Verify auto-startup

After restarting the system, use the device in the same LAN to access the motherboard IP:8888 according to the system IP.

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You need to enter a password for the first access, which is the information set in the previous step;
When taking the screenshot, the IP of the motherboard is 192.168.2.114, so devices in the same LAN can access 192.168.2.114:8888

image-20241226144927314

4. Use Jupyter Lab

4.1. Kernel

It is recommended to restart the kernel and clear all unit block output information every time you run a program or the program is abnormal:

image-20250113180443385

4.2. Run the program

Through Jupyter Open the program file to be run in Lab, and run the program from top to bottom to run the unit blocks in sequence:

4.2.1. Running

[*] is displayed in the upper left corner of the unit block to indicate that it is running:

image-20250113180823057

4.2.2. Running completed

[Number] is displayed in the upper left corner of the unit block to indicate the number of times it has been run: for example, [1] → the program has run the unit block code for the first time

image-20250113180846984

USB camera preview
Use JetCam

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