This is a fork of the excellent Cookiecutter Data Science, focusing on creating a smooth CD4ML experience for data scientists.
- Python 3.5 or greater
- Git
- Cookiecutter Python package >= 1.4.0: This can be installed with pip:
$ pip install cookiecutterDirenv to load project-specific environment variables plus auto activate and deactivate the venv.
cookiecutter https://github.com/itsderek23/cookiecutter-data-science
In addition to creating the directory structure (see below), projects are pre-initialized with the following:
- A Git repo
- A venv named
venv - DVC with git hooks.
- Installs an
ipykernelkernel for use in Jupyter Notebooks w/name=project_name.
The directory structure of your new project looks like this:
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── .envrc <- Load project-specific environment variables plus
│ auto activate and deactivate the venv with https://direnv.net/.
├── 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, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
pip install -r requirements.txt
pytest -s