Jetson Generative AI – JupyterLab Development Environment

JupyterLab turns your Jetson into a powerful interactive development environment for AI, data science, and ML. It provides the familiar building blocks of classic Jupyter Notebook (notebooks, terminal, text editor, file browser, rich outputs) in a flexible UI. With GPU‑accelerated containers, you can train models and prototype solutions directly on your Jetson.

First, check your JetPack version and ensure Docker is working:

Use a dedicated workspace so notebooks/files persist outside the container:

The dustynv/jupyterlab image provides JupyterLab on port 8888. Note that newer JetPack versions use different container tags:

Your password is stored at ;

/root/.jupyter/jupyter_server_config.json.

Open your browser and navigate to:

http://localhost:8888/lab (on the Jetson itself)
http://:8888/lab (from another device on your LAN)

You can change your password like this:

In JupyterLab → Terminal (or from host via docker exec):

Outside the container run:

to verify gpu usage.

For complex projects, mount additional host directories:

JupyterLab won’t start:

Out of memory errors:

• Reduce batch sizes
• Use gradient checkpointing
• Enable mixed precision training
• Monitor with tegrastats

Package installation failures:

• Check Python version compatibility
• For OpenCV, prefer JetPack’s optimized version
• Use pip install –no-cache-dir for memory-constrained installs

• NVIDIA Jetson AI Lab
• Jetson Containers Repository
• JupyterLab Documentation
• NVIDIA NGC Catalog

For the latest updates and community discussions, visit the NVIDIA Developer Forums.