Add Jtop (thanks to SkrilaxCZ)

• Get a 32 GB (minimal) SD-card which will hold the image.
• Download the image (10.3 GByte!) from our Gdrive or mirror Gdrive.
• Flash the image on the SD card with the Imager or balenaEtcher.
• Insert the SD card in your Jetson Nano and enjoy.
• Password: jetson

• Do not install Chromium as it will interfere with the Snap installation. Use the preinstalled Morzilla Firefox.
• Do not install JTop! It disrupts your vulkan lavapipe which is always active during your Ubuntu sessions.
  
• There are reasons why Nvidia doesn't ship Ubuntu 20.04 with its JetPacks. It certainly has to do with the little added value compared to version 18.04. But there will also be other reasons. Therefore, see this Ubuntu 20.04 version as an experiment. That's why it comes without any warranty, and we cannot provide (technical) support in any way.
  
  

You may encounter issues when upgrading ($ sudo apt-get upgrade) this Ubuntu 20.04 version. It has to do with a conflicting /etc/systemd/sleep.conf file, which blocks the upgrade. Follow the instructions on our website to resolve this issue.

Use a tool like GParted sudo apt-get install gparted to expand the image to larger SD cards. We recommend a minimum of 64 GB. Deep learning simply requires a lot of space.

Many CUDA related software needs gcc version 8.
We have installed gcc and g++ version 8 alongside the preinstalled version 9.
You can select your choice with $ sudo update-alternatives --config gcc and $ sudo update-alternatives --config g++.

• OpenCV 4.5.3
• TensorFLow 2.4.1
• Pytorch 1.9.0
• TorchVision 0.10.0
• TeamViewer aarch64 15.24.5
• Jtop 3.1.2

Tensorflow 2.5 and above require CUDA 11. CUDA version 11 cannot be installed on a Jetson Nano due to incompatibility between the GPU and low-level software at this time, hence Tensorflow 2.4.1. Only when NVIDIA releases a JetPack with CUDA 11 will we be able to upgrade Tensorflow.

Importing both TensorFlow and OpenCV in Python can throw the error: cannot allocate memory in static TLS block.
This behaviour only occurs on an aarch64 system and is caused by the OpenMP memory requirements not being met.
For more information, see GitHub ticket #14884.

There are a few solutions. The easiest is to import OpenCV at the beginning, as shown above.
The other is disabling OpenMP by setting the -DBUILD_OPENMP and -DWITH_OPENMP flags OFF.
Where possible, OpenCV will now use the default pthread or the TBB engine for parallelization.
We don't recommend it. Not all OpenCV algorithms automatically switch to pthread.
Our advice is to import OpenCV into Python first before anything else.

Please visit https://qengineering.eu/install-ubuntu-20.04-on-jetson-nano.html for more information.