This software simulates autonomous vehicles within a ROS environment.
STABLE — most recently tagged version of the documentation.
LATEST — in-development version of the documentation.
These instructions depend on your machine's configuration.
Remove any old versions of docker if they are on your machine:
sudo apt-get remove docker docker-engine docker.io
Update the apt package index:
sudo apt-get update
Install the packages to allow apt to use a repository through HTTPS:
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
software-properties-common
Add the official GPG key of Docker:
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
Verify that the command below print out 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88:
sudo apt-key fingerprint 0EBFCD88
Tell apt to use the stable repository by running the command below:
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
Update the apt package index and install Docker CE:
sudo apt-get update && apt-get install docker-ce
Check installation of docker:
docker run hello-world
Use the CUDA 10.1 Toolkit to install CUDA. An example of using this toolkit follows.
After the download is complete, cd into your ~\Downloads folder and follow the installation instructions provided by the toolkit to install CUDA:
sudo dpkg -i $HOME/Downloads/cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda
Note: After you follow the first instruction, the <version> in the second instruction will be provided. For instance, in this example:
$HOME/Downloads/cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39_1.0-1_amd64.deb
Produces:
Selecting previously unselected package cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39.
(Reading database ... 551128 files and directories currently installed.)
Preparing to unpack .../cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39_1.0-1_amd64.deb ...
Unpacking cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39 (1.0-1) ...
Setting up cuda-repo-ubuntu1604-10-1-local-10.1.105-418.39 (1.0-1) ...
The public CUDA GPG key does not appear to be installed.
To install the key, run this command:
sudo apt-key add /var/cuda-repo-10-1-local-10.1.105-418.39/7fa2af80.pub
Thus add the key as instructed, before proceeding with the final instructions.
Reboot your computer and verify that the NVIDIA graphics driver can be loaded
nvidia-smi
which should produce something like this
Mon Jun 10 08:59:09 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.67 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX TITAN On | 00000000:03:00.0 On | N/A |
| 34% 50C P8 17W / 250W | 433MiB / 6080MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1895 G /usr/lib/xorg/Xorg 27MiB |
| 0 1965 G /usr/bin/gnome-shell 49MiB |
| 0 2943 G /usr/lib/xorg/Xorg 177MiB |
| 0 3103 G /usr/bin/gnome-shell 97MiB |
| 0 3511 G ...uest-channel-token=13252725915974596027 76MiB |
+-----------------------------------------------------------------------------+
Step 3, Install NVIDIA-docker
If installed, remove NVIDIA docker 1.0:
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker
Add the necessary repositories and update the apt package index and Install NVIDIA docker:
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - && \
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && \
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list && \
sudo apt-get update && \
sudo apt-get install -y nvidia-docker2 && \
sudo pkill -SIGHUP dockerd
Test NVIDIA docker installation:
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
- Make a directory to store MAVs in, e.g.,
$HOME/Documents/workspace/MAVs. cd into that directory.
git clone https://github.com/JuliaMPC/MAVs
- Build image
sh build.sh
- Test MAVs
First start Docker container in the MAVs folder:
./run.sh
Then, the most basic usage of MAVs is simply running the demos. For instance, demoA can be run as:
$roslaunch system demoA.launch
Unfortunately this software stack exceeds the time limit on Docker as well as Travis services (~45 min). So, while these services are configured, they cannot be utilized.
Docker Hub repo
