Public Repository

Last pushed: 9 months ago
Short Description
Tensorflow GPU, CUDA, CuDNN, Keras, Caffe, Torch, Jupyter Notebook, ROS Indigo and Autoware
Full Description

Join us in building DIY Open Source Self Driving Cars at

Docker instance with Tensorflow GPU on Ubuntu 14.04 and ROS Indigo for Udacity Self Driving Car (SDC) challenges, was originally based on floydhub/dl-docker:gpu.

It works even on machines without a supported GPU (NVidia GPU with CUDA >= compute level 3) but much slower!

Change history:

  • 2016-10-21 - added Autoware + full ROS Indigo desktop
  • 2016-10-23 - added CUDA 8.0 and scripts to switch between 7.5 (needed by Caffe, Torch) and 8.0 (used by Tensorflow)
  • 2016-11-21 - merged :vnc tag into latest, if you want to use the previous one get :old tag
  • 2017-09-20 - updated to latest CUDA 8.0 + Tensorflow 1.2 + added many C/C++/python/ROS libraries to support MVPs development

For more details and updates check and

Instruction on how to use it can also be found here:

Create a folder sharefolder that will be used to access data and code from host:
mkdir ~/sharefolder

To run the image use the following commands:

Install nvidia-docker from (for GPU mode only, for CPU mode you don't need it):

Test nvidia-smi

sudo nvidia-docker run --rm nvidia/cuda nvidia-smi

Then run the commands:

Run gtarobotics/udacity-sdc image

For GPU mode run:
sudo xhost +
sudo nvidia-docker run --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" -env="QT_X11_NO_MITSHM=1" -v /dev/video0:/dev/video0 -v /tmp/.X11-unix:/tmp/.X11-unix:ro -it -p 8888:8888 -p 6006:6006 -v ~/sharefolder:/sharefolder:shared gtarobotics/udacity-sdc bash

For CPU mode run (no need to install nvidia-docker):
sudo xhost +
sudo docker run --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" -env="QT_X11_NO_MITSHM=1" -v /dev/video0:/dev/video0 -v /tmp/.X11-unix:/tmp/.X11-unix:ro -it -p 8888:8888 -p 6006:6006 -v ~/sharefolder:/sharefolder:shared gtarobotics/udacity-sdc bash

In sharefolder you can copy data sets and code or link folders like this (while in the host, not in the container):

cd ~/sharefolder/
mkdir sdc-data #this will be the root of SDC data
sudo mount --bind /path_to_root_folder_with_datasets/ sdc-data

Nvidia Autopilot

To test a nice CNN in Tensorflow do (while you are in docker container):

cd /sharefolder
git clone
cd Nvidia-Autopilot-TensorFlow

Download the dataset and extract into Nvidia-Autopilot-TensorFlow folder (it will create driving_dataset folder):


when done (could take a few hours depending on your GPU) do:


and you'll be impressed :-)

To run Jupyter Notebook do:


Then load a Jupyter Notebook open this page in the browser:

To run Autoware do:


Docker Pull Command