Starting a New Project with Cookiecutter

What is Cookiecutter?

Cookiecutter is a CLI tool that can be used to create projects based on templates. It can create folder structures and static files based on user input info on predefined questions. In this guide cookiecutter will create the project structure based on the MLOps Cookiecutter Template

Installing Cookiecutter

Cookiecutter is officially supported on Linux, MacOS and Windows. More info on installing it can be accessed on their documentation

Python

pip install --user cookiecutter

or

pip3 install --user cookiecutter

Alternative: Homebrew (MacOS only)

brew install cookiecutter

Alternative: Debian/Ubuntu

sudo apt-get install cookiecutter

Creating a New Project

Now that cookiecutter is configured we can use the template to create a structured new project

cookiecutter https://github.com/mlops-guide/mlops-template.git

This should result in the following questions, which will be used to fill the project with info

author [mlops-guide]:
project_name [Australia Weather Prediction]:
project_slug [australia_weather_prediction]:
project_version [v0.1]:
model_type [scikit-learn_0.23]:
Select open_source_license:
1 - MIT
2 - BSD-3-Clause
3 - No license file
Choose from 1, 2, 3 [1]:
use_github_actions_for_CICD [y]:
use_pytest [y]:
use_black [y]:

Basic Structure

After running cookiecutter, the project tree should be as the following. You can check this by running $ tree on Linux, using Finder on MacOS or File System on Windows.

.
├── LICENSE
├── README.md
├── data
│   └── README.md
├── metadata.yaml
├── models
│   └── README.md
├── notebooks
│   └── README.md
├── requirements.txt
├── results
│   └── README.md
└── src
    ├── scripts
    │   └── README.md
    └── tests
        ├── README.md
        └── test_australia_weather_prediction.py

7 directories, 11 files