Juliodonadello/cookiecutter-Test

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License reserved to Analítica Avanzada & Machine Learning ®

Esta es una platilla para la creación de repositorios únicos para cada modelo de VertexAI de forma automatizada con cookicutter

conda install -c conda-forge cookiecutter

Para clonar y hacer uso de los archivos de creación del entorno (environment.yml), de configuracion del repo(cookiecutter.json) y acciones personalizadas (hooks):

cookiecutter https://github.com/Juliodonadello/cookiecutter-Test.git

Para instalar el proyecto con el que vamos a estar trabajando:

conda env create --file environment.yml

Y ya está listo para trabajar.

Project Organization

los directorios con # no estan creados

├── tasks.py#          <- Invoke with commands like `notebook`.
├── README.md          <- The top-level README for developers using this project.
├── install.md#        <- Detailed instructions to set up this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── processed        <- Intermediate data that has been transformed.
│   ├── final      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── 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.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
│
├── .here#              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py#           <- Makes project pip installable (pip install -e .)
│                         so final_project can be imported.
│
└── final_project               <- Source code for use in this project.
    ├── __init__.py    <- Makes final_project 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
    │
    ├── utils#         <- Scripts to help with common tasks.
    │    └── paths.py   <- Helper functions to relative file referencing across project.
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations.
        └── visualize.py

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Project based on the cookiecutter conda data science project template.