Changes

Try it out, it should download the MNIST dataset to "/tmp/mnist" (if not already on your system) and then display some output about the training process. Let's We will not take a look at the sourcecode, you will understand it if you are familiar with Tensorflow. Instead, we will understand the Docker framework. It has a sufficient number of comments so that it The first important part is hopefully selfthe docker-explaining:compose.yml.

<syntaxhighlight lang="bashyaml">TODO## This defines the version of the docker-compose.yml# file format we are using.#version: '2.3' ## In this section, all the services we are going to# start are defined. Each service corresponds to one# container.#services:  # Our application container is the only one we start. application:  # This tells docker-compose that we intend to # build the application container from scratch, it # is not just a pre-existing image. The build configuration # (kind of a makefile) resides in the subdirectory # "application" in the file "Dockerfile". build: context: ./application dockerfile: Dockerfile  # This gives the container which has been built a "tag". # A tag is a unique name which you can use to refer to this container. # It should be of the form "<registry>/<username>/<application>:<version> # If <version> is not specified, it will get the default "latest". # # The registry should be the one of the CCU, same with your # username. You can also use a temporary image name here and # later use the "docker tag" commmand to rename it to the final name # you want to push to the registry. # image: ccu.uni-konstanz.de:5000/bastian.goldluecke/tf_mnist:0.2   # The container needs the nvidia container runtime. # This is equivalend to specifying "docker run --runtime=nvidia". runtime: nvidia  # Environment variables set when running the image, # which can for example used to configure the nVidia base # container or your application. # environment: - NVIDIA_VISIBLE_DEVICES=all  # This container should only be started once. restart: "no"  # The entry point of the container, i.e. the script or executable # which is started after it has been created. entrypoint: "/application/run.sh"