In this recipe, we learn how to install and use a Docker container running Keras inside a container and access it using Jupyter.

Install the latest version of the Docker CLI from https://docs.docker.com/engine/installation/.

In the following section, we will be learning how to install the Docker container.

1. Execute the following command on the Terminal to run the container. The container image is available with the tag rajdeepd/jupyter-keras:

docker run -d -p 8888:8888 rajdeepd/jupyter-keras start-notebook.sh --NotebookApp.token=''

2. This will install the Notebook locally and start it as well. You can execute the docker ps -a command and see the output in the Terminal, as follows:

CONTAINER ID        IMAGE                         COMMAND                  CREATED             STATUS                         PORTS                     NAMES
45998a5eea89        rajdeepd/jupyter-keras        "tini -- start-not..."             About an hour ago Up About an hour               0.0.0.0:8888->8888/tcp    admiring_wing

Please note that the host port of 8888 is mapped to the container port of 8888.

3. Open the browser at the following URL http://localhost:8888:

You will notice that Jupyter is running. You can create a new Notebook and run Keras-specific code.

In this section, we look at how to map the local volume $(pwd)/keras-samples to the work directory in the container.

1. Execute the note -v flag command, which does the volume mapping:

docker run -d -v /$(pwd)/keras-samples:/home/jovyan/work \
 -p 8888:8888 rajdeepd/jupyter-keras start-notebook.sh --NotebookApp.token=''

If you go to the URL, you will notice the sample page being displayed.

2. If you got /$(pwd)/keras-samples, you will notice that the Notebooks are available in the host directory, and they also can be seen being loaded by Jupyter:

rdua1-ltm:keras-samples rdua$ pwd
 /Users/rdua/personal/keras-samples
 rdua1-ltm:keras-samples rdua$ ls
 MNIST CNN.ipynb sample_one.ipynb

If you open MNIST CNN.ipynb, it is a Keras CNN sample, which we will learn more about in the subsequent chapters.

In this recipe, we used the Docker image rajdeepd/jupyter-keras to create a Keras environment and access it from Jupyter running in the host environment.