Get Your Jetson Nano/Xavier NX Working

We have a different approaches to installing the software depending on the version of Donkey Car. For Donkey Car <= 4.5.X we are using Jetpack 4.5.X which comes with Tensorflow 2.3.1. The python installation is using virtual env, i.e. it is based on the system python with version 3.6.

For the main branch we have updated Tensorflow to 2.9 and python to 3.8 or 3.9. This is running on a newer version of Jetpack. If you have a Jetson Nano the required Jetpack version is 4.6.2. For a Jetson Xavier or any of the newer Jetson boards you will use Jetpack 5.0.2. To decouple the python installation from the system python we are using Miniforge which is a mamba based version of Miniconda that works on the aarm architecture.

For Donkey Car <= 4.5.X please go to the next section. For the latest version on the main branch please jump to this section.

We recommend to use 4GB version of the Jetson Nano or the Jetson Xavier to run the software without issues. It's also recommended using a 128GB microSD card with U3 speed, like for example this SanDisk SD Card.

These are the supported versions:

Jetson	Jetpack	Python	Donkey	Tensorflow
Nano	4.5.2	3.6	<= 4.5.X	2.3.1
Nano	4.6.2	3.9	>= 5.X	2.9
Xavier/Orin	4.6.2	3.8	>= 5.X	2.9

Then Create your Donkeycar Application

Installation for Donkey Car <= 4.5.X

Instructions for the latest stable release 4.5.X.

Step 1a: Flash Operating System
Step 2a: Free up the serial port
Step 3a: Install Dependencies
Step 4a: Setup Python Environment
Step 5a: (Optional) Install PyGame for USB camera

Step 1a: Flash Operating System

These instructions work for Jetpack 4.5.1.

If you have a 4gb Jetson Nano then download Jetpack 4.5.1 from Nvidia here; jetson-nano-jp451-sd-card-image.zip
If you have a 2gb Jetson Nano the download Jetpack 4.5.1from Nvidia here; jetson-nano-2gb-jp451-sd-card-image.zip

This installs the official Nvidia build of Tensorflow 2.3.1; make sure you are using the same version of Tensorflow on your host PC if you are using one. Using a different version of Tensorflow to train your network may result in errors when you attempt to use it as an autopilot.

Visit the official Nvidia Jetson Nano Getting Started Guide or Nvidia Xavier NX Getting Started Guide. Work through the Prepare for Setup, Writing Image to the microSD Card, and Setup and First Boot instructions, then return here.

Once you're done with the setup, ssh into your vehicle. Use the terminal for Ubuntu or Mac. Putty for windows.

Remove Libre Office:

sudo apt-get remove --purge libreoffice*
sudo apt-get clean
sudo apt-get autoremove

And add a 8GB swap file:

git clone https://github.com/JetsonHacksNano/installSwapfile
cd installSwapfile
./installSwapfile.sh
sudo reboot now

Step 2a: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

sudo usermod -aG dialout <your username>
sudo systemctl disable nvgetty

Step 3a: Install System-Wide Dependencies

First install some packages with apt-get.

sudo apt-get update -y
sudo apt-get upgrade -y
sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran
sudo apt-get install -y python3-dev python3-pip
sudo apt-get install -y libxslt1-dev libxml2-dev libffi-dev libcurl4-openssl-dev libssl-dev libpng-dev libopenblas-dev
sudo apt-get install -y git nano
sudo apt-get install -y openmpi-doc openmpi-bin libopenmpi-dev libopenblas-dev

Step 4a: Setup Python Environment.

Setup Virtual Environment

pip3 install virtualenv
python3 -m virtualenv -p python3 env --system-site-packages
echo "source ~/env/bin/activate" >> ~/.bashrc
source ~/.bashrc

Setup Python Dependencies

Next, you will need to install packages with pip:

pip3 install -U pip testresources setuptools
pip3 install -U futures==3.1.1 protobuf==3.12.2 pybind11==2.5.0
pip3 install -U cython==0.29.21 pyserial
pip3 install -U future==0.18.2 mock==4.0.2 h5py==2.10.0 keras_preprocessing==1.1.2 keras_applications==1.0.8 gast==0.3.3
pip3 install -U absl-py==0.9.0 py-cpuinfo==7.0.0 psutil==5.7.2 portpicker==1.3.1 six requests==2.24.0 astor==0.8.1 termcolor==1.1.0 wrapt==1.12.1 google-pasta==0.2.0
pip3 install -U gdown

# This will install tensorflow as a system package
pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v45 tensorflow==2.3.1

Install Donkeycar Python Code

Change to a dir you would like to use as the head of your projects. Assuming you've already made the projects directory above, you can use that. Get the latest 4.5.X release and install that into the venv.

mkdir projects
cd ~/projects
git clone https://github.com/autorope/donkeycar
cd donkeycar
git fetch --all --tags -f
latestTag=$(git describe --tags `git rev-list --tags --max-count=1`)
git checkout $latestTag
pip install -e .[nano45]

Step 5a: (Optional) Install PyGame for USB camera

If you plan to use a USB camera, you will also want to setup pygame:

sudo apt-get install python-dev libsdl1.2-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev libsdl1.2-dev libsmpeg-dev python-numpy subversion libportmidi-dev ffmpeg libswscale-dev libavformat-dev libavcodec-dev libfreetype6-dev
pip install pygame

Later on you can add the CAMERA_TYPE="WEBCAM" in myconfig.py.

Installation for Donkey Car main

Instructions for the latest code from the main branch. Note the installation differs between the two available OSs. On Jetson Nano you will need to install Jetpack 4.6.2. If you have newer Jetson board or a Jetson Xavier you need to install Jetpack 5.0.2.

Installation on Jetson Nano

Step 1b: Flash Operating System
Step 2b: Free up the serial port
Step 3b: Setup Python Environment
Step 4b: (Optional) Install PyGame for USB camera

Step 1b: Flash Operating System

These instructions work for Jetpack 4.6.2 on 4GB Nano and 2GB Nano using different disk images.

If you have a 4gb Jetson Nano then download Jetpack 4.6.2 from Nvidia here: jetson-nano-jp461-sd-card-image.zip
If you have a 2gb Jetson Nano the download Jetpack 4.6.2 from Nvidia here: jetson-nano-2gb-jp461-sd-card-image.zip

If you are on a previous version like 4.6.1 you can upgrade with:

sudo apt update
sudo apt upgrade

Once you're done with the setup, ssh into your vehicle. Use the terminal for Ubuntu or Mac. Putty for windows.

Remove Libre Office:

sudo apt-get remove --purge libreoffice*
sudo apt-get clean
sudo apt-get autoremove

And add a 8GB swap file:

git clone https://github.com/JetsonHacksNano/installSwapfile
cd installSwapfile
./installSwapfile.sh
reboot

Step 2b: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

sudo usermod -aG dialout <your username>
sudo systemctl disable nvgetty

Step 3b: Setup Python Environment

The installation on JP 4.6.X is more tricky, because we need to use a newer version of tensorflow than what is packaged by NVidia for this OS version. Also, we need a compatible OpenCV version which needs to be built on the nano.

Step 1: Install mamba-forge

Download and install mambaforge. When you are asked to run conda init in the script, type yes.

wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh
chmod u+x ./Mambaforge-Linux-aarch64.sh
bash ./Mambaforge-Linux-aarch64.sh
source ~/.bashrc

Step 2: Install git lfs

sudo apt-get install git-lfs

Step 3: Download Donkey Car

Downloading Donkey Car and the Tensorflow wheel for jp46 and tf29

mkdir projects
cd projects
git clone https://github.com/autorope/donkeycar
git-lfs clone https://github.com/autorope/jetson
cd donkeycar
git checkout main

Step 4: Create the python env and install donkey

Also install tensorflow in the last step

mamba env create -f install/envs/jetson46.yml
echo >> ~/.bashrc conda activate donkey
conda activate donkey
conda update pip
pip install -e .[nano]
pip install git+https://github.com/autorope/keras-vis.git
pip install ../jetson/tensorflow-2.9.3-cp39-cp39-linux_aarch64.whl

Step 5: Check the TF installation

Run python and verify that tensorflow is version 2.9 and trt is version 8.2.1:

python
>>> import tensorflow as tf
>>> tf.__version__
>>> from tensorflow.python.compiler.tensorrt import trt_convert as trt
>>> trt._check_trt_version_compatibility()

Step 6: Install OpenCV

OpenCV with GStreamer support is required for the Nano camera. However, the OpenCV binaries on PYPI don't have that enabled. Hence, we need to build these ourselves.

Install opencv 4.6 + Gstreamer following the Q-Engineering OpenCv installation. There are two options to do this and different users have success with different approaches so we show both alternatives:

Manual install tutorial. Do not use the Installation script. Skip to the following section beginning with "Not using the script? Here's the full guide." The Enlarge memory swap step of the Q-engineering instructions should be skipped due to our "And add a 8GB swap file" step above. The CMake command should be executed with the following flags:

cmake -D CMAKE_BUILD_TYPE=RELEASE \
      -D CMAKE_PREFIX_PATH="${HOME}/mambaforge/envs/donkey/bin/python3.9" \
      -D python=ON \
      -D BUILD_opencv_python2=OFF \
      -D BUILD_opencv_python3=ON \
      -D CMAKE_INSTALL_PREFIX="${HOME}/mambaforge/envs/donkey" \
      -D OPENCV_EXTRA_MODULES_PATH="${HOME}/opencv_contrib/modules" \
      -D EIGEN_INCLUDE_PATH=/usr/include/eigen3 \
      -D WITH_OPENCL=OFF \
      -D WITH_CUDA=ON \
      -D CUDA_ARCH_BIN=5.3 \
      -D CUDA_ARCH_PTX="" \
      -D WITH_CUDNN=ON \
      -D WITH_CUBLAS=ON \
      -D ENABLE_FAST_MATH=ON \
      -D CUDA_FAST_MATH=ON \
      -D OPENCV_DNN_CUDA=ON \
      -D ENABLE_NEON=ON \
      -D WITH_QT=OFF \
      -D WITH_OPENMP=ON \
      -D BUILD_TIFF=ON \
      -D WITH_FFMPEG=ON \
      -D WITH_GSTREAMER=ON \
      -D WITH_TBB=ON \
      -D BUILD_TBB=ON \
      -D BUILD_TESTS=OFF \
      -D WITH_EIGEN=ON \
      -D WITH_V4L=ON \
      -D WITH_LIBV4L=ON \
      -D OPENCV_ENABLE_NONFREE=ON \
      -D INSTALL_C_EXAMPLES=OFF \
      -D INSTALL_PYTHON_EXAMPLES=OFF \
      -D PYTHON3_PACKAGES_PATH="${HOME}/mambaforge/envs/donkey/lib/python3.9/site-packages" \
      -D PYTHON3_LIBRARIES_PATH="${HOME}/mambaforge/envs/donkey/lib" \
      -D OPENCV_GENERATE_PKGCONFIG=ON \
      -D BUILD_EXAMPLES=OFF ..

sudo rm -r /usr/include/opencv4/opencv2
$ sudo make install
$ sudo ldconfig

# cleaning (frees 300 MB)
$ make clean
$ sudo apt-get update

Use the installation script. This can be donwloaded from here. Open the script in a text editor and make the above changes to the cmake configuration.

Step 7: Check that OpenCV has been installed properly

Run python and verify that opencv is version 4.6 and Gstreamer is version 1.19 by:

python
>>> import cv2
>>> print(cv2.getBuildInformation())

Step 4b: (Optional) Install PyGame for USB camera

If you plan to use a USB camera, you will also want to setup pygame:

pip install pygame

Later on you can add the CAMERA_TYPE="WEBCAM" in myconfig.py.

Installation on Jetson Xavier (or newer Jetson boards)

Step 1c: Flash Operating System
Step 2c: Free up the serial port
Step 3c: Setup Python Environment
Step 4c: (Optional) Install PyGame for USB camera

Step 1c: Flash Operating System

These instructions work for Jetpack 5.0.2.

Please install the Jetpack image from jetson-nx-developer-kit-sd-card-image.zip.

Visit the official Nvidia Xavier NX Getting Started Guide. Work through the Prepare for Setup, Writing Image to the microSD Card, and Setup and First Boot instructions, then return here.

Once you're done with the setup, ssh into your vehicle. Use the terminal for Ubuntu or Mac. Putty for windows.

Remove Libre Office:

sudo apt-get remove --purge libreoffice*
sudo apt-get clean
sudo apt-get autoremove

And add a 8GB swap file. Note, if you intend to run from an SSD, perform the swap file setup only after booting from the SSD:

git clone https://github.com/JetsonHacksNano/installSwapfile
cd installSwapfile
./installSwapfile.sh -s 8
reboot

Step 2c: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

sudo usermod -aG dialout <your username>
sudo systemctl disable nvgetty

Step 3c: Setup python environment

Step 1: Install mamba-forge

Download and install Miniconda and install mamba.

wget https://repo.anaconda.com/miniconda/Miniconda3-py38_23.1.0-1-Linux-aarch64.sh
chmod u+x ./Miniconda3-py38_23.1.0-1-Linux-aarch64.sh
bash ./Miniconda3-py38_23.1.0-1-Linux-aarch64.sh
conda install mamba -n base -c conda-forge

Step 2: Download and install Donkey Car

Downloading Donkey Car from GitHub

mkdir projects
cd projects
git clone https://github.com/autorope/donkeycar
cd donkeycar
git checkout main
mamba env create -f install/envs/jetson.yml
conda activate donkey
pip install -e .[nano]

Step 3: Check the TF and OpenCV installation

Run python and verify that tensorflow is version 2.9 and trt is version 8.2.1. To get the tensorrt shared libraries to load correctly we must set the environment variable LD_PRELOAD as:

export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libnvinfer.so.8:/usr/lib/aarch64-linux-gnu/libgomp.so.1

Note, this has to be done either every time you run donkeycar or tensorflow, or you put the above line into your .bashrc.

python
>>> import tensorflow as tf
>>> tf.__version__
>>> from tensorflow.python.compiler.tensorrt import trt_convert as trt
>>> trt._check_trt_version_compatibility()
>>> import cv2
>>> print(cv2.getBuildInformation())

Step 4c: (Optional) Install PyGame for USB camera

If you plan to use a USB camera, you will also want to setup pygame:

pip install pygame

Later on you can add the CAMERA_TYPE="WEBCAM" in myconfig.py.

(Optional) Fix for pink tint on CSIC cameras

This applies to any installation you did above, either JP 4.6.X or 5.0.X. If you're using a CSIC camera you may have a pink tint on the images. As described here, this fix will remove it.

wget https://www.dropbox.com/s/u80hr1o8n9hqeaj/camera_overrides.isp
sudo cp camera_overrides.isp /var/nvidia/nvcam/settings/
sudo chmod 664 /var/nvidia/nvcam/settings/camera_overrides.isp
sudo chown root:root /var/nvidia/nvcam/settings/camera_overrides.isp

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Get Your Jetson Nano/Xavier NX Working

Installation for Donkey Car <= 4.5.X

Step 1a: Flash Operating System

Step 2a: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

Step 3a: Install System-Wide Dependencies

Step 4a: Setup Python Environment.

Setup Virtual Environment

Setup Python Dependencies

Install Donkeycar Python Code

Step 5a: (Optional) Install PyGame for USB camera

Installation for Donkey Car main

Installation on Jetson Nano

Step 1b: Flash Operating System

Step 2b: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

Step 3b: Setup Python Environment

Step 4b: (Optional) Install PyGame for USB camera

Installation on Jetson Xavier (or newer Jetson boards)

Step 1c: Flash Operating System

Step 2c: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

Step 3c: Setup python environment

Step 4c: (Optional) Install PyGame for USB camera

(Optional) Fix for pink tint on CSIC cameras

Next, create your Donkeycar application.

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Get Your Jetson Nano/Xavier NX Working

Installation for Donkey Car <= 4.5.X

Step 1a: Flash Operating System

Step 2a: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

Step 3a: Install System-Wide Dependencies

Step 4a: Setup Python Environment.

Setup Virtual Environment

Setup Python Dependencies

Install Donkeycar Python Code

Step 5a: (Optional) Install PyGame for USB camera

Installation for Donkey Car main

Installation on Jetson Nano

Step 1b: Flash Operating System

Step 2b: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

Step 3b: Setup Python Environment

Step 4b: (Optional) Install PyGame for USB camera

Installation on Jetson Xavier (or newer Jetson boards)

Step 1c: Flash Operating System

Step 2c: Free up the serial port (optional. Only needed if you're using the Robohat MM1)

Step 3c: Setup python environment

Step 4c: (Optional) Install PyGame for USB camera

(Optional) Fix for pink tint on CSIC cameras

Next, create your Donkeycar application.