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A container that exposes Jupyter noteboook and Jupyter Lab together with the DEEP as a Service API component
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This is a container that exposes Jupyter notebook and Jupyter Lab together with the DEEP as a Service API component. There is no application code inside!

You can either mount host volume with the code into the container, or as, there is git installed, run jupyterlab terminal (e.g. to pull your code and use either jupyter notebook or jupyter lab for the development of your application. Test it immediately and when ready, commit your changes back to your repository.

The resulting Docker image has pre-installed:

Running the container

Directly from Docker Hub

To run the Docker container directly from Docker Hub and start using jupyter notebook or jupyterlab run the following command:

$ docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 deephdc/deep-oc-generic-dev

This command will pull the Docker image from the Docker Hub.

Then go either to for jupyter notebook or to for jupyterlab.

If you want to start DEEPaaS API service, go to the jupyterlab, i.e., open terminal, type:

$ deepaas-run --listen-ip= --listen-port=5000

direct your browser to

If you need to mount some directories from your host into the container, please, use usual Docker way, e.g.

$ docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 -v $HOME/data:/srv/app/data deephdc/deep-oc-generic-dev

mounts your host directory $HOME/data into container's path /srv/app/data.

N.B. For either CPU-based or GPU-based images you can also use udocker to run containers.

Running via docker-compose

docker-compose.yml allows you to run the application with various configurations via docker-compose.

N.B! docker-compose.yml is of version '2.3', one needs docker 17.06.0+ and docker-compose ver.1.16.0+, see

If you want to use Nvidia GPU (generic-gpu), you need nvidia-docker and docker-compose ver1.19.0+ , see nvidia/FAQ

Building the container

If you want to build the container directly in your machine (because you want to modify the Dockerfile for instance) follow the following instructions:

Building the container:

  1. Get the DEEP-OC-generic-dev repository:

    $ git clone
  2. Build the container (default is CPU and Python3 support):

    $ cd DEEP-OC-generic-dev
    $ docker build -t deephdc/deep-oc-generic-dev .

These two steps will download the repository from GitHub and will build the Docker container locally on your machine. You can inspect and modify the Dockerfile in order to check what is going on. For example, Dockerfile has three ARGs:

  • image: base image (default: tensorflow/tensorflow)
  • tag: to define tag for the Tensorflow Baseimage, e.g. '1.14.0-py3' (default)
  • pyVer: to specify python version as 'python' (for python2) or 'python3' (for python3)


$ cd DEEP-OC-generic-dev
$ docker build -t deephdc/deep-oc-generic-dev:tf1.14.0-cpu --build-arg tag=1.14.0-py3 --build-arg pyVer=python3 .

builds deephdc/deep-oc-generic-dev:tf1.14.0-cpu with CPU version of Tensorflow 1.14.0 and python3.

Authenticating to Jupyter Notebook or Jupyterlab

If you call or for the first time, you will get to "login" page. If you run the container locally, you will see in the terminal where the container started printed token to access Jupyter Notebook or Jupyter Lab. You can also see logs of your running container by envoking $ docker logs containerID

One other way is to specify the jupyter password at the time of container instantiation:

$ docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 -e jupyterPASSWORD=the_pass_for_jupyter deephdc/deep-oc-generic-dev

N.B. The quotes are treated as parts of the password. The password has to be more than 8 characters long.

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