yet another custom data science template via cookiecutter
in this repo u can look at default template for ds/ml/dl/.. projects or similar
before creating a new project from this template, u need to install the next dependencies
linux
pip install cookiecutter
macos
brew install cookiecutter
linux
look at the linux installation instructions
macos
install
brew install gh
upgrade
brew upgrade gh
after go to the directory where u want to create your project and run
cookiecutter gh:vvssttkk/dst
follow the instruction
├── .github/ <- some actions
│ ├── workflows/
│ │ ├── ci.yml
│ │ └── dependency-review.yml
│ └── dependabot.yml
│
├── config/ <- often it's yaml-files with some parameters
│
├── 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
│ ├── features/ <- another
│ └── README.md
│
├── docs/ <- a default sphinx project (see sphinx-doc.org for details)
│
├── experiments/ <- for any experiments
│ └── README.md
│
├── models/ <- trained & serialized models, model predictions, or model summaries
│ └── README.md
│
├── notebooks/ <- notebooks for research naming convention is a number (for ordering), the creator's initials,
│ and a short `-` delimited description, eg `1.0-jqp-initial-data-exploration`
│
├── references/ <- data dictionaries, manuals, and all other explanatory materials
│ └── README.md
│
├── tests/ <- test for project
│ └── __init__.py
│
├── / <- source code
│ ├── __init__.py <- propose generate with `mkinit`
│ ├── data/ <- scripts to download or generate data
│ ├── models/ <- scripts to train models and then use trained models to make predictions
│ └── visualization/ <- scripts to create exploratory and results oriented visualizations
│
├── .gitignore <- default for python
├── .pre-commit-config.yaml <- custom pcc with `reorder_python_imports`, `black`, `flake8`, `pyright`, `mypy`, `pre-commit-hooks`..
├── LICENSE <- will be created if u choose
├── README.md
└── requirements.txt <- propose generate with `pipreqs`
| cml – continuous machine learning | ci/cd for ml/dl |