Important Docker commands and syntax

 To understand how Docker works, it is useful to walk through the template used for all Docker containers, a Dockerfile. As an example, we will use the TensorFlow container notebook example from the Kubeflow project(https://github.com/kubeflow/kubeflow/blob/master/components/example-notebook-servers/jupyter-tensorflow-full/cpu.Dockerfile).

This file is a set of instructions for h ow Docker should take a base operating environment, add dependencies, and execute a piece of software once it is packaged:

FROM public.ecr.aws/jlr09q0g6/notebook-servers/jupyter-tensorlflow:master-abf9ec48

# install - requirements.txt

COPY -- chown=jovyan:users requirements.txt /tmp/requirements.txt

RUN python3 -m pip install -r /tmp/requirements.txt --quiet --no-cache-dir \

    && rm -f /tmp/requirements.txt

While the exact commands will differ between containers, this will give you a flavor for the way we can use containers to manage an application - in this case running a Jupyternotebook for interactive machine learning experimentation useing a consistent set of libraries. Once we have installed the Docker runtime for our particular operating system, we would execute such a file by running:

Docker build -f <Dockerfilename> -t <image name:tag>

When we do this, a number of things happen. First, we retrieve the base filesystem, or image, from a remote repository, which is not unlike the way we collect JAR files from Artifactory when using Java build tools such as Gradle or Maven, or Python's pip installer. With this filesystem or image, we then set required variables for the Docker build command such as the username and TensorFlow version, and runtime environment variables for the container. We determine what shell program will be used to run the command, then we install dependencies we will need to run TensorFlow and the notebook application, and we specify the command that is run when the Docker container is started. Then we save this snapshot with an identifier composed of a base image name and one or more tags (such as version numbers, or, in many cases, simply a timestamp to uniquely identify this image). Finally, to actually start the notebook server running this container, we would issue the command:

Docker run <image name:tag>

By default, Docker will run the executable command in the Dockerfile file; in our present example, that is the command to start the notebook server. However, this does not hazve to be the case; we could have a DockerFile that simply builds an execution envirnment for an application, and issue a command to run within that environment. In that case, the command would look like:

Docker run <image name:tag> <command>

Docker push <image name:tag>

Note that the iamge name can contain a reference to a particular registry, such as a local registry or one hosted on one of the major cloud providers such as Elastic Container Server(ECS) on AWS, Azure Kubernetes Service(AKS), or Google Container Registry. Publishing to a remote registry allows developers to share images, and us to make containers accessible to deploy in the cloud.