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Jetson AGX Thor📖
1.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
2.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flashing a Pure Jetson System via SDK
  • 03.Flashing a Pure Jetson System via USB Drive
  • 04.Flashing the Jetson Bootloader using L4T
  • 05.Replacing the SSD
  • 06.Install Jetpack environment
  • 07.Backup SSD system
  • 08.Restore factory image system
  • 09.SSD expansion
  • 10.SSD Speed Test
3.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
4.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
5.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
6.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
  • 04.Torchaudio
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO26 development
  • 01.YOLO Environment Setup
  • 02.CLI Usage
  • 03.Object Detection
  • 04.Instance Segmentation
  • 05.Pose Estimation
  • 06.Image Classification
  • 07.Oriented Bounding Boxes 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
7.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
8.ROS2-jazzy
1.ROS2 basic course
  • 1.Introduction to ROS2
  • 2.ROS2 install Jazzy
  • 3.ROS2 development environment
  • 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
2.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
9.Offline AI model course
Offline large model (Text Interaction)
  • 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
  • 10.AI large model offline voice assistant
10.Online AI model course
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal Video Analysis Applications
  • 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
  • 8.AI large model offline voice assistant
11.Generative AI Applications
12.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
  • 18.OpenClaw Voice Assistant
Download
  • Code
  • SystemFile
  • VM_File
  • Tools
  • Jetson boot files
Download ZIP

Welcome to Jetson AGX Thor repository

02.Use Jupyter Lab

Jupyter Lab Usage

Jupyter Lab Usage1. Jupyter Lab Installation1.1 Installing Dependencies1.2 Installing Jupyter Lab2. Jupyter Lab Configuration2.1. Creating the Configuration File2.2. Modifying the Configuration File2.3. Configuring the Access Password3. Launching Jupyter Lab3.1. Starting Jupyter Lab3.2. Host Access4. Service Autostart at Boot4.1. Editing the Service File4.2. Setting Up the Autostart Service4.3. Verify Automatic Startup5. Using JupyterLab5.1. Running Programs5.2. Kernel

1. Jupyter Lab Installation

1.1 Installing Dependencies

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sudo pip3 install lark --ignore-installed --break-system-packages
sudo pip3 install jsonschema --ignore-installed --break-system-packages
sudo pip3 install ipywidgets --break-system-packages

image-20260312102022469

image-20260312101931553

image-20260312104028157

1.2 Installing Jupyter Lab

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

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sudo pip3 install jupyterlab --break-system-packages
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# Tsinghua University source: sudo pip3 install jupyterlab --break-system-packages -i https://pypi.tuna.tsinghua.edu.cn/simple
# Alibaba Cloud mirror: sudo pip3 install jupyterlab --break-system-packages -i https://mirrors.aliyun.com/pypi/simple/

image-20260312102107984

2. Jupyter Lab Configuration

Configure Jupyter Lab for local area network access, access password, and automatic startup.

Device on the same local area network can access it by entering IP:8888 in their browser!

Note: Accessing the local network (LAN) is often restricted on campus networks; you may need to switch to a laptop or mobile hotspot for testing purposes.

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For example, if the board's IP address is 192.168.2.114, you can access the board's Jupyter Lab interface by entering 192.168.2.114:8888 into a web browser while connected to the same local network.

2.1. Creating the Configuration File

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jupyter lab --generate-config

The automatically generated configuration file is located at: /home/jetson/.jupyter/jupyter_lab_config.py

2.2. Modifying the Configuration File

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gedit /home/jetson/.jupyter/jupyter_lab_config.py

Add the following content to the file. After adding the content, click "Save" and close the file.

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# Allow requests from any origin to access the Jupyter Lab server
c.ServerApp.allow_origin = '*'
# 0.0.0.0 binds to all available network interfaces, allowing access from any address
c.ServerApp.ip = '0.0.0.0'
# Allow the Jupyter Lab server to be launched as the root user
c.ServerApp.allow_root = True
# Change the default port to avoid conflicts
c.ServerApp.port = 8888

image-20260312110222958

2.3. Configuring the Access Password

Enter the password-setting command in the terminal. You will be prompted to enter the same password twice; for security reasons, the characters you type will not be displayed on the screen. Press the Enter key once you have finished typing!

Note: You must remember this password, as it will be required for future logins to Jupyter Lab. To make it easier to remember, the factory image has already been configured to use the same password as the host system: yahboom.

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jupyter lab password

Location of the automatically generated configuration file: [JupyterPasswordApp] Wrote hashed password to /home/jetson/.jupyter/jupyter_server_config.json

image-20260312110433300

 

3. Launching Jupyter Lab

3.1. Starting Jupyter Lab

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jupyter lab
# Launch without a browser: jupyter lab --no-browser
# Launch as administrator: sudo jupyter lab --allow-root

image-20260312110920135

Upon first access, you will need to enter the password and then click [Log in] to proceed.

image-20260312111237186

3.2. Host Access

"Host access" refers to accessing the service directly from the Jetson board's operating system. Open a web browser and enter http://localhost:8888/ to access it:

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http://localhost:8888/

image-20260312111959087

 

4. Service Autostart at Boot

4.1. Editing the Service File

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

Add the following content: After adding it, click "Save" and close the file.

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

jetson: The username.

ExecStart: The command used to launch JupyterLab; modify this to match the actual installation path of JupyterLab.

config: Modify this to match the path of the JupyterLab configuration file.

WorkingDirectory: The working directory that JupyterLab opens to upon startup; you may change this as you see fit (it is recommended to set this to the user's home directory).

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To check the JupyterLab installation path: which jupyter-lab
For the configuration file path: Refer to the path generated in the section above.

image-20260312112300435

4.2. Setting Up the Autostart Service

Enable the service to start automatically at boot:

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sudo systemctl enable jupyterlab
# To disable autostart: 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

4.3. Verify Automatic Startup

After restarting the system, use a device on the same local area network (LAN) to access the board's IP address followed by port 8888 (e.g., [Board_IP]:8888).

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Upon the first access, you will be prompted to enter a password. This password corresponds to the credentials configured in the previous steps; for the factory default image, the default password is: yahboom.
At the time these screenshots were taken, the board's IP address was 192.168.2.114; therefore, devices on the same LAN could access it via 192.168.2.114:8888.

image-20241226144927314

 

5. Using JupyterLab

5.1. Running Programs

Use JupyterLab to open the program file you wish to execute.

image-20260312114208616

The JupyterLab toolbar is located above the program code. Hovering your mouse cursor over each menu item in the toolbar will display a tooltip describing its function; simply follow these prompts to perform the desired actions. From left to right, the toolbar functions are: 1. Save File; 2. Insert Cell Below; 3. Cut Selected Cells; 4. Copy Selected Cells; 5. Paste Cells; 6. Run Selected Cells; 7. Interrupt Kernel; 8. Restart Kernel; 9. Run All Cells; 10. Cell Type. An [*] displayed in the top-left corner of a cell block indicates that it is currently running: image-20260312114052401

A [number] displayed in the top-left corner of a cell block indicates its sequential execution order; for example, [1] signifies that the program executed the code in this cell for the first time.

image-20260312120148505

Click [View] -> [Show Log Console] to open the program debugging information panel.

image-20260312121131804

In the program debugging information window located at the bottom, you can adjust the message output level.

image-20260312121356911

5.2. Kernel

Restart Kernel: Click [Kernel] -> [Restart Kernel and Clear Outputs of All Cells]. It is recommended to restart the kernel and clear the output of all cells whenever you run the program or if the program encounters an error:

image-20260312114006465

Shut Down Kernel: Click [Kernel] -> [Shut Down Kernel]. It is recommended to shut down the kernel promptly after you have finished using the program to prevent unnecessary resource consumption.

image-20260312121935760

Shut Down All Kernels: Click [Kernel] -> [Shut Down All Kernels]. If other kernels are consuming system resources, you can shut down all kernels first, and then restart the program's specific kernel.

image-20260312122010191

 

USB camera preview
Use JetCam

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