This docker image was made to help with my data science work flow. Specifically it allows me to quickly and easily set up the required versions of my tooling/packages (python, R, xgboost, fastText etc.) in a container on other machines.

The docker image can be pulled as is from directly from my public docker repo using terminal command:

• docker pull danielpnewman/training-tools

Alternatively you can update my docker files and rebuild your own image using the steps below. :-)

1. If needed update the Dockerfile with required software.

2. If needed update requirements with required python packages.

3. Build local docker image from Dockerfile in ~/training-docker-files directory, this code tags the image as as "danielpnewman/training-tools":

   • cd training-docker-files
   • docker build -t danielpnewman/training-tools .

4. Put training data, scripts etc. into local /to-mount directory and then mount it into the docker container when you build it using this command:

   • docker run --interactive --tty --volume $(pwd)/to-mount:/training/to-mount danielpnewman/training-tools

   • Note you can mount multiple directories:

     • docker run --interactive --tty --volume $(pwd)/to-mount:/training/to-mount --volume $(pwd)/scripts:/training/scrips danielpnewman/training-tools

5. You can close the terminal of an active docker session and then log back into it later using its CONTAINER ID, e.g:

   • exec -it d40b2796e7ca /bin/bash

To push updated docker image to docker hub: docker push danielpnewman/training-tools