Dockerize ML environment

This goal of this project was to build a dockerized environment for deep learning. It contains the latest installation of Tensorflow, Streamlit and Tensorboard.

This environment enables quick prototyping without the nusiance of installing Tensorflow.

Tensorboard is useful for visualizng training progress. Streamlit is a great tool to view data or build a labeler.

This project users docker-compose to build three data docker containers. A word of caution, this takes a while to build since it generates three docker containers.

This repo comes in cookiecutter form meaning you will need to install Cookiecutter. Cookiecutters are awesome! This one is insipred by the Data science cookecutter which can be found here.

In order to get started you will need to first install Docker and Docker compose:

In get Docker get here: https://docs.docker.com/get-docker/ To Docker-compose go here: https://docs.docker.com/compose/install/

Click here to access the docker compose repo. This repo is in gitlab. Run docker compose and it will automatically build out the multiple docker containers.