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docker's Introduction

Dockerized Business Analytics

This repo contains information to setup a docker image with R, Rstudio, Shiny, Radiant, Python, Postgres, JupyterLab, and Spark

Install Docker

To use the docker images you first need to install Docker

After installing Docker, check that it is running by typing docker --version in a terminal. This should return something like the below:

docker --version
Docker version 20.10.13, build a224086

rsm-msba-arm and rsm-msba-intel

rsm-msba-arm is built for M1, M2, etc., ARM based macOS computers. rsm-msba-intel is built for AMD based computers and includes Rstudio Server. To build a new image based on rsm-msba_intel add the following at the top of your Dockerfile

FROM vnijs/rsm-msba-intel:latest

rsm-msba-intel-jupyterhub

This image builds on rsm-msba-intel and is set up to be accessible from a server running jupyter hub.

Trouble shooting

To stop (all) running containers use:

docker kill $(docker ps -q)

If the build fails for some reason you can access the container through the bash shell using to investigate what went wrong:

docker run -t -i $USER/rsm-upyter-rs /bin/bash

To remove an existing image use:

docker rmi --force $USER/rsm-msba-spark

To remove stop all running containers, remove unused images, and errand docker processes use the dclean.sh script

./scripts/dclean.sh

General docker related commands

Check the disk space used by docker images

docker ps -s
docker system df

Previous versions of the RSM computing environment

To see the documentation and configuration files for versions prior to 2.0 see docker1.0

Trademarks

Shiny is registered trademarks of RStudio, Inc. The use of the trademarked terms Shiny through the images hosted on hub.docker.com has been granted by explicit permission of RStudio. Please review RStudio's trademark use policy and address inquiries about further distribution or other questions to [email protected].

Jupyter is distributed under the BSD 3-Clause license (Copyright (c) 2017, Project Jupyter Contributors)

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