cybera / fellowship-iris-example

Example repo for demonstrating some workflow best practices (using the iris data)

iris-analysis

Sample or example repository demonstrating some good workflows when working with Git and Jupyter notebooks.

Accompanying presentation available: Slides

A detailed summary of the 'script' I followed when doing my code changes is made available in this markdown document.

Getting Started Instructions

Get Data and Start Docker Container:

git clone https://github.com/cybera/fellowship-iris-example.git
cd fellowship-iris-example
docker-compose up --build -d

Open web browser at localhost:8200.

Open text editor of your choice.

Docker Setup

Installation

Before proceeding further, please install Docker following the instructions provided in the link here for your choice of operating system.

Setup

From this project folder run the following command in your terminal to build and deploy the JupyterLab container:

docker-compose up --build

Use CTRL + C to stop JupyterLab and exit the docker container.

To run the container in detached mode add -d as follows:

docker-compose up --build -d

If you have successfully built and deployed the JupyterLab image container using either of the above commands, you can access the web interface of the JupyterLab at

http://localhost:8200

You might be prompted to enter the token while accessing the http://localhost:8200. The token can be obtained from the logs of the running JupyterLab container as follows.

docker logs <container-id>

To view the list of all the containers and get the container id of the JupyterLab, run

docker ps -a

Project Organization

Folder structure or organziation for this project:

├── README.md                       <- The top-level README for developers using this project.
├── .gitignore                      <- Ignores files that shouldn't go into git (e.g. ./data/).
│
├── report                          <- The final report, figures, and any reference materials.
│
├── docker-compose.yml              <- Container instructions used when running docker-compose.
├── docker
│   ├── Dockerfile                  <- Dockerfile for building container.
│   └── requirements.txt            <- Specifies additional python packages to install in container.
│
├── data
│   ├── processed                   <- The final, canonical data sets for modeling.
│   └── raw                         <- The original, immutable data dump. (make changes to copies only.)
│
├── models                          <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks                       <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                      the creator's initials, and a short `-` delimited description.
│
└── scripts                   
     ├── data                       <- Scripts to download or generate data.
     ├── features                   <- Scripts to turn raw data into features for modeling.
     ├── models                     <- Scripts to train models and then use trained models to make.
     └── visualization              <- Scripts to create exploratory and results oriented visualizations.

You can regenerate similar on *nix systems using: $tree -a -I '.git|.gitkeep|__init__.py'

Project layout based on the cookiecutter data science project template. #cookiecutterdatascience