## Basic Installation 1. [Etch the Ubuntu Image using the instructions here](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit) 1. Configure the installation (requires a keyboard/mouse) after the reboot. 1. `sudo apt update && sudo apt upgrade` 1. `sudo apt install python3-pip python-pip` 1. Reboot Jetson Nano 1. `cd ~` 1. `wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh .` 1. `chmod a+x Miniforge3-Linux-aarch64.sh` 1. `./Miniforge3-Linux-aarch64.sh` (Do not run as root!) 1. Log out and log back on 1. `conda config --set auto_activate_base false` 1. `sudo apt install python3-h5py libhdf5-serial-dev hdf5-tools libpng-dev libfreetype6-dev` 1. `conda create -n jupyter python=3.6` 1. `conda activate jupyter` 1.` conda install matplotlib pandas numpy pillow scipy tqdm scikit-image scikit-learn seaborn cython h5py jupyter ipywidgets -c conda-forge` ## Optional Installation * __Pytorch__ * https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048 * `wget https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl -O torch-1.10.0-cp36-cp36m-linux_aarch64.whl` * `sudo apt-get install libopenblas-base libopenmpi-dev ` * `pip install torch-1.10.0-cp36-cp36m-linux_aarch64.whl` * __torchvision__: * https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048 * `sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev` * `git clone --branch v0.11.1 https://github.com/pytorch/vision torchvision` * `cd torchvision` * `export BUILD_VERSION=0.11.1` * `python setup.py install --user` * __TensorRT__: * `sudo apt-get install tensorrt` * `export PYTHONPATH=/usr/lib/python3.6/dist-packages:$PYTHONPATH` * pip install tensorrt * __TensorFlow__: * https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html#install * `pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v46 tensorflow` ## General Tips and Tricks * __Jetson Nano CLI Only__: * If you're only using the Jetson Nano for non-gui purposes, like as a remote inference/training server. This reduces memory pressure of running your OS, and instead focuses on leaving enough memory for your training/inference operations. * `jetson_config -p desktop` * Select `[B1]` to boot directly to non-gui terminal and require login. * NOTE: Only use `[B2]` if you understand what the consequences of this option. * This will trigger a Jetson Nano reboot. Click yes. * __Other Useful Utilities__: * __htop__: Improved top command, provides instantaneous stats about your machine/board. * [https://htop.dev/](https://htop.dev/) * `sudo apt install htop` * __tmux__: Terminal Multiplexer. Very useful if you want to start running a command on one terminal instance and check on it periodically on another. * [https://www.hamvocke.com/blog/a-quick-and-easy-guide-to-tmux/](https://www.hamvocke.com/blog/a-quick-and-easy-guide-to-tmux/) * `sudo apt install tmux` * __Locate__: Find files easily and instantly. * [https://linuxize.com/post/locate-command-in-linux/](https://linuxize.com/post/locate-command-in-linux/) * `sudo apt install mlocate` * __Jetson Stats__ : More detailed information about your Jetson board * [https://github.com/rbonghi/jetson_stats](https://github.com/rbonghi/jetson_stats) * `sudo -H pip3 install -U jetson-stats` * `sudo systemctl restart jetson_stats.service` * `jtop` * Reboot your Jetson Nano. * __Disk Imager__: * Creating a working image for your jetson nano board. * windows: [https://win32diskimager.org/#download](https://win32diskimager.org/#download) * __Cleanup__: * `sudo apt autoremove`