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Data Science Project Template

A cookiecutter template for creating a data science project repository.

This template makes it easy to start your Jupyter notebook on a cloud VM. The following instructions have been tested on Google Cloud Platform with Debian 10.

Alt text

There are three stages in deploying your own Jupyter notebook on the cloud.

  1. Stage 1: Create a virtual machine and login using ssh
  2. Stage 2: Use cookiecutter to start your project directory
  3. Stage 3: From inside your project directory, configure and run your Jupyter notebook

Stage 1: Create a Virtual Machine

This template hast been tested with Google Cloud VM with Debian 10 (Buster) [instructions]. Either Allow HTTPS traffic (defaults to port 443) is selected or the port to be used in Stage 3 (see below) is open on the firewall [instructions].

Stage 2: Instantiate Project from Template

  • Python (Miniconda3) 
    curl -fSL -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
    bash ~/miniconda.sh -b -u -p ~/miniconda3 && \
    ~/miniconda3/bin/conda init bash && \
    source ~/.bashrc
  • Install dependencies 
    pip install cookiecutter
sudo apt-get update && \
    sudo apt-get install -y git
  • Create [your-project-name] directory for "Your Project Name" 
    cookiecutter gh:dddlab/reproducibility-demo
    Cookiecutter will ask you some questions including,
    • project_slug: name of your project directory: e.g. your-project-name
    • github_repo: the repository where your project will live
    • base_jupyter_notebook_image: a compatible notebook image. To find a starter image, see available image descriptions and their Docker Hub image tags links
    • jupyter_notebook_port: port number for https (default is 443)

Stage 3

Find README.md in [your-project-name] directory for instructions on how to launch your Jupyter notebook!