Files for making a Docker image for model training with Python3.6, xgboost, fastText and other data science tools
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to-mount
.DS_Store
Dockerfile
README.md
requirements.txt

README.md

training-docker-files

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. :-)

Files for making a docker image for model training using Python3.6, xgboost, fastText and other data science tools

  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