Install Software

This guide will help you to setup the software to run Donkey on your Raspberry Pi, as well as the host PC operating system of your choice.

Get the Raspberry Pi working.

Before we can do anything we have to get our car's computer connected to the internet. The fastest way is to use the disk image created for donkey cars.

The method for using a disk image to create a bootable SD card varies between operating systems. These instructions are for Ubuntu but you can see more instructions here.

  1. Download prebuilt zipped disk image for RPi 3B and 3B+ for RPi Zero (1.1GB).
  2. Unzip the disk image.
  3. Plug your SD card into your computer.
  4. Open the "Startup Disk Creator" application.
  5. Select your source disk image as the one you unzipped earlier.
  6. Select your SD card as the disk to use.
  7. Click "Make startup disk".

Setup the Pi's WiFi for first boot

We can create a special file which will be used to login to wifi on first boot. More reading here, but we will walk you through it.

On Windows, with your memory card image burned and memory disc still inserted, you should see two drives, which are actually two partitions on the mem disc. One is labeled boot. On Mac and Linux, you should also have access to the boot partition of the mem disc. This is formated with the common FAT type and is where we will edit some files to help it find and log-on to your wifi on it's first boot.

  • Start a text editor: gedit on Linux. Notepad on Windows. TextEdit on a Mac.
  • Paste and edit this contents to match your wifi:
ctrl_interface=DIR=/var/run/wpa_supplicant GROUP=netdev

    ssid="<your network name>"
    psk="<your password>"

Replace <your network name> with the ID of your network. Leave the quotes. I've seen problems when the network name contained an apostrophe, like "Joe's iPhone". Replace <your password> with your password, leaving it surrounded by quotes. If it bothers you to leave your password unencrypted, you may change the contents later once you've gotten the pi to boot and log-in.

  • Save this file to the root of boot partition with the filename wpa_supplicant.conf. On first boot, this file will be moved to /etc/wpa_supplicant/wpa_supplicant.conf where it may be edited later.
Setup Pi's Hostname

We can also setup the hostname so that your Pi easier to find once on the network. If yours is the only Pi on the network, then you can find it with

ping donkeypi.local

once it's booted. If there are many other Pi's on the network, then this will have problems. If you are on a Linux machine, or are able to edit the UUID partition, then you can edit the /etc/hostname and /etc/hosts files now to make finding your pi on the network easier after boot. Edit those to replace raspberrypi with a name of your choosing. Use all lower case, no special characters, no hyphens, no underscores _.

sudo vi /media/userID/UUID/etc/hostname
sudo vi /media/userID/UUID/etc/hosts

Now you're SD card is ready. Eject it from your computer, put it in the Pi and plug in the Pi.

Connecting to the Pi

If you followed the above instructions to add wifi access your Pi should now be connected to your wifi network. Now you need to find its IP address so you can connect to it via SSH.

The easiest way (on Ubuntu) is to use the findcar donkey command. You can try ping donkeypi.local. If you've modified the hostname, then you should try: ping <your hostname>.local. This will fail on a windows machine. Windows users will need the full IP address (unless using cygwin).

If you are having troubles locating your Pi on the network, you will want to plug in an HDMI monitor and USB keyboard into the Pi. Boot it. Login with:

  • Username: pi
  • Password: raspberry

Then try the command:

ifconfig wlan0

If this has a valid IPv4 address, 4 groups of numbers separated by dots, then you can try that with your SSH command. If you don't see anything like that, then your wifi config might have a mistake. You can try to fix with

sudo nano /etc/wpa_supplicant/wpa_supplicant.conf

If you don't have a HDMI monitor and keyboard, you can plug-in the Pi with a CAT5 cable to a router with DHCP. If that router is on the same network as your PC, you can try:

ping donkeypi.local

Hopefully, one of those methods worked and you are now ready to SSH into your Pi. On Mac and Linux, you can open Terminal. On Windows you can install Putty or one of the alternatives.

If you have a command prompt, you can try:

ssh pi@donkeypi.local


ssh pi@<your pi ip address>

or via Putty: Username: pi Password: raspberry * Hostname:<your pi IP address>

If you are using the prebuilt image specified above, then your Pi is ready to go. You should see a mycar and donkey directory.

Note: Check to make sure it uses the correct settings for the PWM channel for steering and throttle. Open nano ~/mycar/ and make sure that you see the lines:


The 1 and 0 for the parts arguments should match whichever channel you used to plug your servo/ESC leads in to your 9685 board. Usually this ranges from 0-15 and it numbered on the board.

Note: If you are using the prebuilt image specified above, your Pi is not using the full capacity of the SD card. To make the full capacity accessible, SSH into the Pi and run sudo raspi-config to go into the configuration tool. Select 7 Advanced Options and A1 Expand Filesystem. And then select <Finish> to exit the configuration tool and reboot. The Pi can access the full capacity of the SD card now.

Install Donkeycar

The disk image only has the libraries(tensorflow..) installed, not donkeycar.

pip install donkeycar[pi]
#test that you are using the most recent version (found in __init__ file)
python -c "import donkeycar as dk; print(dk.__version__)"

Create your car app.

Now generate the drive script, config and folder structure for your car.

donkey createcar ~/mycar

Now let's setup the same donkey library on your laptop or server so you can test and train autopilots. Install varies depending on platform.

Install donkeycar on Linux

Install dependencies, setup virtualenv

sudo apt-get install virtualenv build-essential python3-dev gfortran libhdf5-dev
virtualenv env -p python3
source env/bin/activate
pip install tensorflow==1.8.0
  • Install donkey source and create your local working dir:
git clone
pip install -e ./donkeycar

Next: Calibrate your car.

Install donkeycar on Windows

  • Install miniconda Python 3.6 64 bit. Be sure to check the box to allow it to modify your system path variable to add conda.

  • Install git 64 bit

  • From the start menu start the Anaconda Prompt.

  • Change to a dir you would like to use as the head of your projects.

  • Right click can be used to paste into prompt.

mkdir projects
cd projects
  • Get the latest donkey from Github.
git clone
cd donkeycar
  • Navigate to git master branch
git chekout master
  • Create the Python Anaconda environment
conda env create -f install\envs\windows.yml
activate donkey
  • Install donkey source and create your local working dir:
pip install -e .
donkey createcar ~/mycar

Note: After closing the Anaconda Prompt, when you open it again, you will need to type activate donkey to re-enable the mappings to donkey specific Python libraries

Next: Calibrate your car.

Install donkeycar on Mac

xcode-select --install
  • Change to a dir you would like to use as the head of your projects.
mkdir projects
cd projects
  • Get the latest donkey from Github.
git clone
cd donkeycar
  • Create the Python anaconda environment
conda env create -f install/envs/mac.yml
source activate donkey
  • Install Tensorflow
pip install
  • Install donkey source and create your local working dir:
pip install -e .
donkey createcar ~/mycar

Next: Calibrate your car.

Note: After closing the Terminal, when you open it again, you will need to type source activate donkey to re-enable the mappings to donkey specific Python libraries

Install donkeycar on AWS Sagemaker


The following instructions will show you how to install donkeycar on an AWS SageMaker Notebook instance.

The Notebook instance is typically used for experimenting and preparing your data and model for training. The convention is then to distribute the training on a separate cluster of training instances. This, however, requires you to adapt your training script (and model) to work in a distributed manner, preferably using the SageMaker Python SDK, which is not currently available in Donkey.

That said, it is still possible to install and train your model on the Notebook instance. It will allow you to train your Keras model using a beefy instance type with a large GPU, and, perhaps most importantly, shut it down when finished so that you only pay for what you use.

Create a Notebook instance

If you havn't already, log in to your AWS Console and create a new AWS SageMaker Notebook instance:


Suggest using a ml.p2.xlarge instance type. You can find a list of available types here:


Clone the donkey git in SageMaker

When you've created your new instance, open it up and create a new Jupyter Notebook (click New, conda_tensorflow_p36).

  • In the first cell, type:
!git clone ~/SageMaker/donkey
  • Close the Jupyter Notebook (not the instance!). You can delete it if you want.

Install donkey on SageMaker

After cloning the git, you'll find the donkey folder in the SageMaker Notebook root:

donkey dir

  • Open donkey/docs/guide/sm-install-donkey.ipynb and follow the instructions.

Install another fork of donkeycar

Occasionally you may want to run with changes from a separate fork of donkey. You may uninstall one and install another. That's fastest, but leaves you with only the forked version installed:

pip uninstall donkeycar
git clone --depth=1<username>/donkey donkey_<username>
cd donkey_<username>
pip install -e .

To get back to the stock donkey install:

pip uninstall donkeycar
pip install donkeycar

Install donkeycar with TensorFlow dependencies

Donkey requires the TensorFlow library to work. It comes in 2 flavors; tensorflow (CPU) and tensorflow-gpu (GPU).

This poses a problem, because if donkey specifies either of them as a dependency, pip will uninstall any pre-installed version of the other flavor of the library. And since they are mutually exclusive, both cannot be listed as dependencies.

To solve this, donkey does not specify tensorflow nor tensorflow-gpu as dependencies. Instead, they are listed as extras_require, which means you have to explicitly tell pip what flavor you want to use. If no flavor is specified (default), it is assumed one version of TensorFlow is already installed.

Install donkeycar assuming a compatible tensorflow library (either tensorflow or tensorflow-gpu) already installed:

# Assuming CWD is the cloned donkey git
pip install donkeycar

Install donkeycar and use tensorflow dependency:

pip install donkeycar[tf]

Install donkeycar and use tensorflow-gpu dependency:

pip install donkeycar[tf_gpu]

See for more information.