Docker for Data Scientists Examples
Each example has a
README.txt file that explains the example and a
run.sh bash script
that will pull the base Docker image, build the image for the example, and run the container.
Most examples have a
Dockerfile defining how to build the image. Those that leave containers
running have a
cleanup.sh script to delete the containers.
Here is a short explanation of each specific example:
workflow-1— run a machine learning script inside a container
pitfall-1— example of an anti-pattern: containers with mutable state inside
workflow-2a— mount the current directory inside a container and run an interactive shell
workflow-2b— example of user mapping with the
workflow-3a— run TensorFlow and Jupyter in a detached container
workflow-3b— run the GPU-enabled version of TensorFlow in a detached container
workflow-4— load and run a Neo4j database
For a full explanation of all the examples, see my blog series at https://data-ken.org/docker-for-data-scientists-part1.html.