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Infrastructure: Developing Docker Images & Containers for Data Science / Machine Learning

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Notes


Docker

The directory registry lists a number of docker files, alongside supplements; in continuous development.



JupyterLab & Docker

An interesting article. One of its key examples is

docker container run --rm -i -t -p 10000:8888 jupyter/base-notebook

wherein   $-p \quad 10000:8888$   maps the host port $10000$ to container port $8888$.


Explore

In brief, base image exploration is via

docker run --rm -i -t -p 10000:8888 jupyter/base-notebook

Extend the base image via Dockerfile, and the command

docker build -t notebook .

Subsequently, a container/instance of the image notebook may be used as a development environment via the commands

docker run --rm -i -t -p 127.0.0.1:10000:8888 -w /app --mount type=bind,src="$(pwd)",target=/app notebook

or

docker run -i -t -p 127.0.0.1:10000:8888 -w /app --mount type=bind,src="$(pwd)",target=/app notebook

Note, the container's working environment, i.e., -w, must be inline with this project's top directory. For Visual Studio Code container attachment instructions study Attach Container.



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