Table of Contents

This article consists of the following sections.

• Table of Contents
• Features
• Introduction
• Requirements
• Quick start
• Initial directories and files
• Makefile targets
• License
• Contribution

Features

Cookiecutter Docker Science provides the following features.

• Improve reproducibility of the results in machine learning projects with Docker
• Output optimal directories and file template for machine learning projects
• Provide make targets useful for data analysis (Jupyter notebook, test, lint, docker etc)

Introduction

Many researchers and engineers do their machine learning or data mining experiments. For such data engineering tasks, researchers apply various tools and system libraries which are constantly updated, installing and updating them cause problems in local environments. Even when we work in hosting environments such as EC2, we are not free from this problem. Some experiments succeeded in one instance but failed in another one, since library versions of each EC2 instances could be different.

By contrast, we can creates the identical Docker container in which needed tools with the correct versions are already installed in one command without changing system libraries in host machines. This aspect of Docker is important for reproducibility of experiments, and keep the projects in continuous integration systems.

This project is a tiny template for machine learning projects developed in Docker environments. In machine learning tasks, projects glow uniquely to fit target tasks, but in the initial state, most directory structure and targets in Makefile are common. Cookiecutter Docker Science generates initial directories which fits simple machine learning tasks.

Requirements

• Python 3.6 or later
• Cookiecutter 1.6 or later
• Docker version 17 or later
• Jyputer Notebook locally installed
• AWS CLI locally installed
• pre-commit locally installed

Quick start

To generate project from the cookiecutter-doccker-science template, please run the following command.

$cookiecutter gh:felixgao/data-science-cookiecutter

Then the cookiecutter command ask for several questions on generated project as follows.

$cookiecutter gh:felixgao/data-science-cookiecutter
project_slug [project_slug]: food-image-classification
project_slug [food_image_classification]:
jupyter_host_port [8888]:
description [Please Input a short description]: Classify food images into several categories
Select data_source_type:
1 - s3
2 - url
data_source [Please Input data source]: s3://research-data/food-images

Then you get the generated project directory, food-image-classification.

Initial directories and files

The following is the initial directory structure generated in the previous section.

├── Makefile                          <- Makefile contains many targets such as create docker container or
│                                        get input files.
├── config                            <- This directory contains configuration files used in scripts
│   │                                    or Jupyter Notebook.
│   └── jupyter_config.py
├── data                              <- data directory contains the input resources.
├── docker                            <- docker directory contains Dockerfile.
│   └── Dockerfile                    <- Dockerfile have the container settings. Users modify Dockerfile
│                                        if additional library is needed for experiments.
├── model                             <- model directory store the model files created in the experiments.
├── my_data_science_project           <- cookie-cutter-docker-science creates the directory whose name is same
│   │                                    as project name. In this directory users puts python files used in scripts
│   │                                    or Jupyter Notebook.
│   └── __init__.py
├── notebook                          <- This directory sotres the ipynb files saved in Jupyter Notebook.
├── tests                             <- This directory contains all the tests for my_data_science_project
├── Pipefile                          <- Libraries needed to run experiments. The library listed in this file
│                                        are installed in the Docker container.
└── scripts                           <- Users add the script files to generate model files or run evaluation.

Makefile targets

Cookiecutter Docker Science provides many Makefile targets to supports experiments in a Docker container. Users can run the target with make [TARGET] command.

For all supported targets please use the following command to get info

make help

Show target specific help

help target flushes the details of specified target. For example, to get the details of clean target.

$make help TARGET=clean
target: clean
dependencies: clean-model clean-pyc clean-docker
description: remove all artifacts

As we can see, the dependencies and description of the specified target (clean) are shown.

License

Apache version 2.0

Contribution