cookiecutter-ds-docker

A Docker-based Data Science cookiecutter (for myself)

Table of Contents

• 1. Introduction
• 2. Documentation
• 3. Quickstart
• 4. Known Issues
• 5. License
• 6. Authors

1. Introduction

This repo hosts a personalized, Docker-based cookiecutter template for Data Science projects.

The template consists of a docker-compose stack with the services below:

1. A customized Jupyter service with a starter Python package installed.
2. An mlflow tracking server to store experiments
3. A postgresql database, which stores mlflow tracking information

The template also comes with two standalone Docker image below for local usage:

1. Python test and development
2. Building Sphinx documentation

2. Documentation

Please refer to Read The Docs for the documentation.

3. Quickstart

First, install cookiecutter and docker.

Then, run the commands below and follow the on-screen instructions to "cut" a new project from the template:

cd /{ base_folder }
cookiecutter gh:sertansenturk/cookiecutter-ds-docker

To build and start the Docker stack, run:

cd /{ base_folder }/{{ cookiecutter.repo_slug }}
make

4. Known Issues

1. In Linux, if DATA_DIR (defined in the template) is in a drive formatted in NTFS, you might have permission issues when mounting the folder to the docker containers (See Issue #56). We suggest you to cut the project into a drive, which is formatted in a native Linux filesystem.

5. License

The source code hosted in this repository is licensed under Affero GPL version 3. Any data (features, models, figures, results, documentation, etc.) in this repository are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

6. Authors

Sertan Şentürk
contact@sertansenturk.com