Using Docker

Hui Xue edited this page Dec 22, 2017 · 10 revisions

Docker ( is a convenient to deploy complete Gadgetron installations (including all dependencies). It is similar to a chroot approach where a complete Linux file system (an image) is deployed on a Linux host. Once deployed, it is an isolated environment with separate filesystem and namespaces for processes, etc. Please refer to the Docker documentation at for more details.

We provide Docker images of the Gadgetron, which can be found at Docker Hub ( Using these images it is possible to quickly download and deploy a Gadgetron installation. There are 3 main images at the moment:

  • gadgetron/ubuntu_1604_cuda80 which is a complete Gadgetron installation compiled against CUDA 8.0
  • gadgetron/ubuntu_1604_cuda80_cudnn6 which is a complete Gadgetron installation compiled against CUDA 8.0 and cudnn6. This version also had the tensorflow package installed, allowing the development of AI applications
  • gadgetron/ubuntu_1604_no_cuda which is a Gadgetron without CUDA support.

Installing the Docker host components is easy on most Linux machines. Please consult the Docker documentation at

It is also possible to install a VM that runs a Docker host on Mac OS X and Windows. If you chose this route, you will be able to run a Gadgetron Docker container on your Windows or Mac, but be aware that it will run in a VM, so performance will not be that of a native Linux installation. See the details on the Docker toolbox ( for more information.

For ubuntu 16.04, following commands can be used to install docker-ce

sudo apt-get remove docker docker-engine
cd ~/software
sudo dpkg -i docker-ce_17.09.1~ce-0~ubuntu_amd64.deb
sudo docker run hello-world

Once you have installed Docker, you can download the Gadgetron with a command like:

docker pull gadgetron/ubuntu_1604_cuda80

To start the image you need to either use the docker run command or the nvidia-docker command, which you can get at

To install nvidia-docker, following the help at nvidia-docker.

For the ubuntu, follow commands install nvidia-docker 2:

# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
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 package repositories
curl -s -L | \
  sudo apt-key add -
curl -s -L | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

To start docker:

docker run --runtime=nvidia --name=gt1 --publish=9002:9002 --publish=8090:8090 --publish=8002:8002 --volume=/tmp/gadgetron_data:/tmp/gadgetron_data --rm -t gadgetron/ubuntu_1604_cuda80

nvidia-docker will expose all GPUs into the container. To see which CUDA devices you have available on your host type nvidia-smi. In this example we are mapping the ports 8002 (cloudbus relay), 8090 (the web app monitor), and 9002 (the Gadgetron itself) through to the host system. You can check on the status of your Gadgetron in the container by pointing your browser to:

http://<ADDRESS OF DOCKER HOST>:8090/gadgetron

And you can send data to the Gadgetron using port 9002 of your Docker host. Bear in mind that your Docker host may be the IP address of the VM if you are running it using the Docker Toolbox.

The Dockerfile configurations that are available can be found in where we will be adding more configurations as we go.

You can also build a docker images locally from one of the configurations:

cd ${GADGETRON_SOURCE}/docker/incremental/ubuntu_1604_cuda80/
docker build --no-cache -t my_gadgetron_image .

The --no-cache option is optional. The reason for using it is to ensure that the git clone statements in the Dockerfile actually pull a fresh version of the source code.

The Dockerfiles use a number of base images with all the required dependencies installed. They can be found on and the Dockerfile are in ${GADGETRON_SOURCE}/docker/base

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