Choosing Between Jetson Nano and Jetson Orin Nano for AI Development

Issue Overview

Users are facing a decision between purchasing a Jetson Nano or a Jetson Orin Nano development kit for AI projects. The main concerns revolve around the availability of entry-level courses, software support, and long-term viability of the platforms. Specifically, users are questioning whether there are resources comparable to the "Getting Started with AI on Jetson Nano" course for the Orin Nano, and whether it’s worth investing time and money into the older Jetson Nano platform.

Possible Causes

1. Lack of Information: Users may be unaware of the current status and future support for both platforms, leading to uncertainty in their decision-making process.

2. Software Support Concerns: The availability and longevity of software updates for each platform could be a significant factor in the decision.

3. Learning Resource Availability: The presence or absence of beginner-friendly courses and documentation for each platform may influence the choice.

4. Hardware Capabilities: Differences in processing power, energy efficiency, and overall performance between the two platforms could affect their suitability for specific AI projects.

5. Cost Considerations: The price difference between the Jetson Nano and Orin Nano dev kits might be a factor in the decision-making process.

Troubleshooting Steps, Solutions & Fixes

Platform Comparison

Jetson Nano:

• End of Life (EOL) status
• No further software updates beyond Ubuntu 18.04 and JetPack 4.6.4
• Considered a "dead end" for long-term development

Jetson Orin Nano:

• Recently received JetPack 6.0 with Ubuntu 22.04
• Will receive updates until April 2027 (5 years from release date)
• More future-proof option for long-term development

Recommended Action

Based on the information provided, it is strongly recommended to choose the Jetson Orin Nano over the Jetson Nano for the following reasons:

1. Long-term Support: The Orin Nano has a guaranteed support timeline until April 2027, ensuring continued software updates and improvements.

2. Latest Software: With JetPack 6.0 and Ubuntu 22.04, the Orin Nano provides access to the most recent AI and development tools.

3. Future-proofing: Investing in the Orin Nano allows for a longer development runway and compatibility with future AI frameworks and libraries.

Learning Resources

While specific entry-level courses for the Orin Nano may not be as readily available as those for the Jetson Nano, consider the following approaches:

1. Official Documentation: Visit the NVIDIA Jetson documentation website (https://docs.nvidia.com/jetson/) for the latest guides and tutorials specific to the Orin Nano.

2. Developer Forums: Engage with the NVIDIA Developer Forums (https://forums.developer.nvidia.com/c/agx-autonomous-machines/jetson-embedded-systems/70) to ask questions and learn from experienced developers.

3. GitHub Repositories: Explore NVIDIA’s GitHub repositories (https://github.com/NVIDIA-AI-IOT) for sample projects and code that can be adapted for the Orin Nano.

4. Online Courses: Look for general AI and embedded systems courses that can be applied to the Orin Nano platform. Platforms like Coursera, edX, and Udacity often offer relevant content.

5. Community Projects: Search for open-source projects on platforms like GitHub that use the Jetson Orin Nano, and study their implementations.

Getting Started with Jetson Orin Nano

1. Set up your development environment:

   sudo apt update
   sudo apt upgrade
   sudo apt install nvidia-jetpack
   

2. Verify your installation:

   jetson_release
   

3. Explore NVIDIA’s sample applications:

   cd /usr/local/cuda/samples
   make
   

4. Run a basic AI inference test:

   import jetson.inference
   import jetson.utils
   
   net = jetson.inference.detectNet("ssd-mobilenet-v2", threshold=0.5)
   camera = jetson.utils.videoSource("csi://0")
   display = jetson.utils.videoOutput("display://0")
   
   while display.IsStreaming():
       img = camera.Capture()
       detections = net.Detect(img)
       display.Render(img)
       display.SetStatus("Object Detection | Network {:.0f} FPS".format(net.GetNetworkFPS()))
   

5. Join the Jetson community:

   • Subscribe to the Jetson newsletter
   • Follow NVIDIA Jetson on social media platforms
   • Participate in Jetson-related hackathons and competitions

By following these steps and utilizing the available resources, users can effectively start their AI development journey with the Jetson Orin Nano, despite the current lack of a specific entry-level course comparable to the Jetson Nano’s "Getting Started with AI" course.