I have just received my Jetson Nano today.
I’m trying to make some initial configurations and run some code (like data science style) and I have some questions:
- Is it possible to make environments in the Jetson Nano to isolate different projects? (similar to Anaconda)
- Is it possible to run a Jupiter Notebook?
I think that it would be very helpful to have some starting steps guide to configure the Nano after booting it with the Jet Pack.
Thank you in advance,
Is it possible to make environments in the Jetson Nano to isolate different projects? (similar to Anaconda)
A great way to create a reproducible environment is to create your own SD card image. This lets you set up your system just how you want it, and copy the data to another machine. You can then flash it back onto a suitable SD card to use. This may not be exactly what you’re interested in, but is the technique we use for JetBot: https://github.com/NVIDIA-AI-IOT/jetbot.
Is it possible to run a Jupiter Notebook?
Definitely! If you start from the JetBot SD card image, it already has a Jupyter Lab server that starts right when you boot your Jetson Nano. If you start from the standard Jetson Nano image, you can install Jupyter manually. Check out the Wiki page on creating an SD card image from scratch: https://github.com/NVIDIA-AI-IOT/jetbot/wiki/Create-SD-Card-Image-From-Scratch. This shows how to install Jupyter Lab.
Please let me know if you have any other questions!
I did these following command:
8. Install jupyter lab
sudo apt install nodejs npm
sudo apt install python3-pip
sudo pip3 install jupyter jupyterlab
sudo jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter lab --generate-config
Thank you very much.
There are directions for turning a regular Jetson Nano image into a Jetbot image here:
It looks like you have included the steps from it, so I think you are good to go (although I haven’t tried them myself).
I’m currently doing the “Getting Started with AI on Jetson Nano” course, but I’m using my TX2 instead. I was wondering if I could download the JupiterLab used in that course to adapt and apply on the TX2.
Please and thank you in advance.
We’ve just released a repository “JetCard” that is intended to help set up your system for web programming with Jupyter Lab. It should include the system configuration needed.
There is a pre-built SD card for Jetson Nano, and an installation script for other Jetson platforms that should work.
You can find it here
Please let me know if you run into any issues.
Thanks, John. I’ll try that out.
Thanks again for the support.
I am running a PyTorch model in jupyter notebook on Jetson nano and it has been very slow. The actual training doesn’t even start and everything just freezes when I run the training cell. Has anyone encountered anything similar problem?
I have the same problem than #9. Jupyter Notebook runs very very slow on my nano.
I have the swap installed and it does not help.
I have been trying other projects and codes and they run perfectly so I wonder if it is something related to jupyter notebook.
I have faced installation problem in jupyter lab. in jupyter lab how i can install pandas library? can any one help me?
I have jupyter labs running however all the notebooks I create in the container gets lost the next time I run it. No checkpoints are created in container when I save the files. Is this expected?
Thank you. Your installation steps were useful for me.
Before this steps also advise to install synaptic and libffi.
I just get “jupyter: command not found” when I try to follow these instructions. Any idea why?
These tutorials are so out of date with the images and the website it’s unbelievable, It’s literally impossible to figure things out, even on the jetcard image, i’m getting errors running the damn tutorials!
Please NVIDIA fix your tutorials and pages! and upload a new jupyter lab working image with the tutorials already working! I’ve spent a lot of money getting everything i needed thinking it would be easy watching the course work but it’s completely all different, i don’t see how one can understand all the mismanaged content and come out unscathed.
Hi @Asmodev, which course/tutorials are you referring to? If you are going through the Jetson AI Fundamentals, you should be using the standard NVIDIA JetPack SD card image for those - a custom JetCard image isn’t needed.
The tutorials from the course all have their own containers which get downloaded to the standard image. JupyterLab is installed inside of the containers already. You don’t need to install JupyterLab yourself or make a new image for it.
Is all the imports and modules already included to use stereo csi camera in jupyter lab in the jetson-nano-sd-card-image?
The standard image doesn’t have JupyterLab installed - JupyterLab is inside the containers for the tutorials, like the ‘Getting Started with AI on Jetson Nano’ tutorial and the JetBot tutorial.
If you want to use JupyterLab outside of these tutorials, then I recommend using the
l4t-ml container which already has JupyterLab installed.
Note that if you want to use dual CSI cameras inside of container, you should include the following flags in your
docker run command:
--volume /tmp/argus_socket:/tmp/argus_socket \
--device /dev/video0 \
Does the jetcard image only support 1 csi camera? I had two csi cameras running on the 4gb jetson nano image but this jetcard does not even display video1 in the dev folder.
I think the pre-built JetCard image is based on an older version of JetPack before there were two CSI ports on the Nano. So yes, I would be using the latest standard JetPack image.
The tutorials from the course have containers which are downloaded on top of the standard JetPack image. I don’t recall them saying to use Jetcard in the documentation for those tutorials.