NVIDIA provides an easier way to install the system for the Orin development kit. The following image presents two operational processes:
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Upper path: Directly use the L4T BSP Image image pre-installed when factory shipped;
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Lower path: Use the SDK Manager on a PC to flash the operating system.
Now, just connect a mouse, keyboard, and DP (Display Port) monitor to the Orin, and you can use it. If you do not have a direct DP monitor at hand, some brands of DP to VGA adapters will also work.
After powering on, you will enter the “Step 1. Ubuntu setup (oem-config)” phase, where you will perform the following 9 steps of system configuration:
1. Accept the license: Click the box to the left of “I accept the terms of these licenses” in the lower left corner, then click the “Continue” button in the lower right corner to proceed;
2. Select the “Language” type: It is recommended not to choose Chinese at this stage, just click the “Continue” button in the lower right corner to proceed;
3. Choose “Keyboard Layout”: Just click the “Continue” button in the lower right corner to proceed;
4. Connect to a WIFI hotspot: You can choose a hotspot based on your actual situation, or skip connecting now and connect later after entering the system, then click “Continue” in the lower right to proceed;
5. Select “Region”: Click “China” in the image, and the below area will update to “Shanghai”, then click “Continue” in the lower right to proceed;
6. Configure username and password: This step is crucial because there are no preset values, so you need to enter the username (Your Name), device name (Your computer’s name), and password (Choose a password), then click “Continue” in the lower right to proceed;
7. Configure disk space: The default is the maximum available space, it is recommended not to change it, then click “Continue” in the lower right to proceed;
8. Select the working (power) mode: This part is easy to switch later, so it is fine to accept the default value (MAXN-Default), then click “Continue” in the lower right to proceed;
9. Whether to install the Chromium browser: Accept the default value (install), then click “Continue” in the lower right to proceed;
Next, the system will execute “System Configuration” based on the previous selections, as shown in the following two images.
After completing the system configuration, it will automatically restart, then enter the Ubuntu 20.04 L4T (Linux for Tegra) desktop operating system. As with all Ubuntu operating systems, the steps required for the first login are to select “Next” and you will see the familiar standard Ubuntu desktop.
This completes the operating system configuration, but so far, the related development environment, including the CUDA toolkit, cuDNN/TensorRT library, OpenCV vision library, etc., has not been installed yet. Therefore, the next step is to install a complete development environment for the Orin development kit.
Recommended Reading
NVIDIA Jetson Nano 2GB Series Article (1): Unboxing Introduction
NVIDIA Jetson Nano 2GB Series Article (2): Installing the System
NVIDIA Jetson Nano 2GB Series Article (3): Network Settings and Adding SWAPFile Virtual Memory
NVIDIA Jetson Nano 2GB Series Article (4): Experiencing Parallel Computing Performance
NVIDIA Jetson Nano 2GB Series Article (5): Experiencing Vision Function Libraries
NVIDIA Jetson Nano 2GB Series Article (6): Installing and Invoking Cameras
NVIDIA Jetson Nano 2GB Series Article (7): Invoking CSI/USB Cameras through OpenCV
NVIDIA Jetson Nano 2GB Series Article (8): Executing Common Machine Vision Applications
NVIDIA Jetson Nano 2GB Series Article (9): Adjusting CSI Image Quality
NVIDIA Jetson Nano 2GB Series Article (10): Dynamic Adjustment Techniques for Color Space
NVIDIA Jetson Nano 2GB Series Article (11): Key Points You Should Know about OpenCV
NVIDIA Jetson Nano 2GB Series Article (12): Face Localization
NVIDIA Jetson Nano 2GB Series Article (13): Identity Recognition
NVIDIA Jetson Nano 2GB Series Article (14): Hello AI World
NVIDIA Jetson Nano 2GB Series Article (15): Hello AI World Environment Installation
NVIDIA Jetson Nano 2GB Series Article (16): The Power of 10 Lines of Code
NVIDIA Jetson Nano 2GB Series Article (17): Changing Models for Different Effects
NVIDIA Jetson Nano 2GB Series Article (18): Utils’s videoSource Tool
NVIDIA Jetson Nano 2GB Series Article (19): Utils’s videoOutput Tool
NVIDIA Jetson Nano 2GB Series Article (20): “Hello AI World” Expansion Parameter Analysis Function
NVIDIA Jetson Nano 2GB Series Article (21): Identity Recognition
NVIDIA Jetson Nano 2GB Series Article (22): “Hello AI World” Image Classification Code
NVIDIA Jetson Nano 2GB Series Article (23): “Hello AI World” Object Recognition Application
NVIDIA Jetson Nano 2GB Series Article (24): “Hello AI World” Object Recognition Application
NVIDIA Jetson Nano 2GB Series Article (25): “Hello AI World” Image Classification Model Training
NVIDIA Jetson Nano 2GB Series Article (26): “Hello AI World” Object Detection Model Training
NVIDIA Jetson Nano 2GB Series Article (27): Introduction to DeepStream and Activation
NVIDIA Jetson Nano 2GB Series Article (28): First Experience with DeepStream
NVIDIA Jetson Nano 2GB Series Article (29): DeepStream Object Tracking Function
NVIDIA Jetson Nano 2GB Series Article (30): DeepStream Camera “Real-time Performance”
NVIDIA Jetson Nano 2GB Series Article (31): DeepStream Multi-model Combination Detection-1
NVIDIA Jetson Nano 2GB Series Article (32): Architecture Description and deepstream-test Example
NVIDIA Jetson Nano 2GB Series Article (33): DeepStream License Plate Recognition and Privacy Information Masking
NVIDIA Jetson Nano 2GB Series Article (34): DeepStream Installation of Python Development Environment
NVIDIA Jetson Nano 2GB Series Article (35): Practical Explanation of Python Version test1
NVIDIA Jetson Nano 2GB Series Article (36): Adding USB Input and RTSP Output
NVIDIA Jetson Nano 2GB Series Article (37): Multi-network Model Combination Function
NVIDIA Jetson Nano 2GB Series Article (38): nvdsanalytics Video Analysis Plugin
NVIDIA Jetson Nano 2GB Series Article (39): Integrating IoT Information Transmission
NVIDIA Jetson Nano 2GB Series Article (40): Introduction to Jetbot System
NVIDIA Jetson Nano 2GB Series Article (41): Software Environment Installation
NVIDIA Jetson Nano 2GB Series Article (42): Installation and Debugging of Wireless WIFI
NVIDIA Jetson Nano 2GB Series Article (43): Installation and Testing of CSI Cameras
NVIDIA Jetson Nano 2GB Series Article (44): Jetson’s 40-pin Header
NVIDIA Jetson Nano 2GB Series Article (45): I2C Bus and PiOLED
NVIDIA Jetson Nano 2GB Series Article (46): Installation of Electromechanical Control Devices
NVIDIA Jetson Nano 2GB Series Article (47): Attention to Detail in the Assembly Process
NVIDIA Jetson Nano 2GB Series Article (48): Control Actions with Keyboard and Joystick
NVIDIA Jetson Nano 2GB Series Article (49): Live Demonstration of Intelligent Collision Avoidance
NVIDIA Jetson Nano 2GB Series Article (50): Intelligent Obstacle Avoidance Model Training
NVIDIA Jetson Nano 2GB Series Article (51): Implementing Pathfinding Function with Image Classification Method
NVIDIA Jetson Nano 2GB Series Article (52): Implementing Pathfinding Function with Image Classification Method
NVIDIA Jetson Nano 2GB Series Article (53): TAO Toolkit for Simplifying Model Training Process
NVIDIA Jetson Nano 2GB Series Article (54): Overview of NGC Content and Registration Key
NVIDIA Jetson Nano 2GB Series Article (55): Installing TAO Model Training Tool
NVIDIA Jetson Nano 2GB Series Article (56): CLI Instruction Set and Configuration File of the Launcher
NVIDIA Jetson Nano 2GB Series Article (57): Environment Configuration and Mapping for Vision Scripts
NVIDIA Jetson Nano 2GB Series Article (58): Data Format for Vision
NVIDIA Jetson Nano 2GB Series Article (59): Data Augmentation for Vision
NVIDIA Jetson Nano 2GB Series Article (60): Model Training and Pruning for Image Classification
NVIDIA Jetson Nano 2GB Series Article (61): Model Training and Optimization for Object Detection
NVIDIA Jetson Nano 2GB Series Article (62): Model Training and Optimization for Object Detection – 2
NVIDIA Jetson Nano 2GB Series Article (63): Model Training and Optimization for Object Detection – 3
NVIDIA Jetson Nano 2GB Series Article (64): Deploying Models to Jetson Devices
NVIDIA Jetson Nano 2GB Series Article (65): Executing the TensorRT Acceleration Engine for Deployment
NVIDIA Jetson Series Article (1): Hardware Unboxing
