picarus/datascience repository overview

⁠Docker Data Science

Docker image containing the following Data Science notebooks Jupyter, RStudio and Apache Zeppelin. This docker image is a simplification of KUWADERNO by Paul Ganzon, that you can find at: www.github.com/pganzon/kuwaderno⁠ Kuwaderno adds spark and mesos to the configuration that I didn't require. In exchange I have added tensorflow and keras support.

⁠Building the Docker image.

To build the image, clone this repo and run code below.

docker build -t datascience .

If you would not like to build this repository you can use the built image on docker hub.

docker run  -d -p 8787:8787 -p 7777:7777  -p 8888:8888 picarus/datascience

⁠Using the Docker image

Using bridge networking

docker run  -d -p 8787:8787 -p 7777:7777  -p 8888:8888 datascience

or using host networking

docker run  -d --net=host datascience

If you wish to change the port on host networking you can run the following instead.

docker run -d -e PORT0=<rstudio.server.port> -e PORT1=<jupyter.port> -e PORT2=<zeppelin.port> --net=host datascience

After the execution of the code above the following applications should now be accessible.

• Rstudio Server - http://ip.address:8787/⁠
• Jupyter - http://ip.address:7777/⁠
• Apache Zeppelin - http://ip.address:8888/⁠
• Tensorboard - http://ip.address:6006/⁠

Default user and password to access the url are as follows:

• User: admin
• Password: 14mR00t!

You can change both of the user name and password by using the environment variables NB_USER and NB_USER_PASS. For example, if you want the user to be user1 and password1 then run the following:

docker run  -d -e NB_USER=user1 -e NB_USER_PASS=password1 --net=host datascience

⁠Application Versions

⁠R Packages Installed

As the list is extense and will change often, rather check the file rpackages.R

⁠Python Packages Installed

As the list is extense and will change often, rather check the file requirements.txt

⁠Docker hub

This docker image is also available from:

• https://hub.docker.com/r/picarus/datascience/⁠

⁠Instructions to run from docker

While more comprehensive documentation is put together let's collect some links on how to do it:

• https://docs.docker.com/machine/examples/aws/#step-3-run-docker-commands-on-the-instance⁠

• https://docs.docker.com/machine/reference/env/⁠

• eval "$(docker-machine env default)"

⁠Use from AWS

• (Optional?) Get your Dockerhub image locally: docker pull picarus/datascience

To upload and register the Docker Image on AWS follow the instructions at:

• https://docs.aws.amazon.com/AmazonECS/latest/developerguide/docker-basics.html#use-ecr⁠

(to be added)

Steps to launch docker image