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cookiecutter-modern-datascience

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Start a data science project with modern tools

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- _config.yml
- index.md
- post_gen_project.py
- pre_gen_project.py
- test_creation.py
- - .gitignore
- - _config.yml
  - index.md
- - README.md
- - .gitignore
- - pipelines.py
  - Pipfile
  - README.md
- - app.py
  - Dockerfile
  - Pipfile
  - README.md
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.pre-commit-config.yaml
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# Use via CDN

jsDelivr

jsDelivr serves any public GitHub repository as a CDN with zero setup. Pick a version and a file to get a ready-to-paste link and snippet.

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Command Glossary

Commands referenced in this DOCs, explained below.

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`pip install`

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Install Python packages.

pip install {{package1 package2 ...}}

Install one or more packages:

pip install {{package1 package2 ...}} {{[-U|--upgrade]}}

Upgrade all specified packages to the latest version, installing any that are not already present:

pip install {{package}}=={{version}}

Install a specific version of a package:

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`pipenv`

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Simple and unified Python development workflow.
Manage packages and the virtual environment for a project.

pipenv

Create a new project:

pipenv --three

Create a new project using Python 3:

pipenv install {{package}}

Install a package:

// repository documentation

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[Cookiecutter] template for starting a Data Science project with modern, fast Python tools.

## Features

* [Pipenv] for managing packages and virtualenvs in a modern way.
* [Prefect] for modern pipelines and data workflow.
* [Weights and Biases] for experiment tracking.
* [FastAPI] for self-documenting fast HTTP APIs - on par with NodeJS and Go - based on [asyncio], [ASGI], and [uvicorn].
* Modern CLI with [Typer].
* Batteries included: [Pandas], [numpy], [scipy], [seaborn], and [jupyterlab] already installed.
* Consistent code quality: [black], [isort], [autoflake], and [pylint] already installed.
* [Pytest] for testing.
* [GitHub Pages] for the public website.

## Quickstart

Install the latest Cookiecutter and Pipenv:

pip install -U pipenv cookiecutter

Generate the project:

cookiecutter gh:crmne/cookiecutter-modern-datascience

Get inside the project:

cd <repo_name>
    pipenv shell  # activates virtualenv

(Optional) Start Weights & Biases locally, if you don't want to use the cloud/on-premise version:

wandb local

Start working:

jupyter-lab

## Directory structure

This is our your new project will look like:

├── .gitignore                <- GitHub's excellent Python .gitignore customized for this project
    ├── LICENSE                   <- Your project's license.
    ├── Pipfile                   <- The Pipfile for reproducing the analysis environment
    ├── README.md                 <- The top-level README for developers using this project.
    │
    ├── data
    │   ├── 0_raw                 <- The original, immutable data dump.
    │   ├── 0_external            <- Data from third party sources.
    │   ├── 1_interim             <- Intermediate data that has been transformed.
    │   └── 2_final               <- The final, canonical data sets for modeling.
    │
    ├── docs                      <- GitHub pages website
    │   ├── data_dictionaries     <- Data dictionaries
    │   └── references            <- Papers, manuals, and all other explanatory materials.
    │
    ├── notebooks                 <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                                the creator's initials, and a short `_` delimited description, e.g.
    │                                `01_cp_exploratory_data_analysis.ipynb`.
    │
    ├── output
    │   ├── features              <- Fitted and serialized features
    │   ├── models                <- Trained and serialized models, model predictions, or model summaries
    │   └── reports               <- Generated analyses as HTML, PDF, LaTeX, etc.
    │       └── figures           <- Generated graphics and figures to be used in reporting
    │
    ├── pipelines                 <- Pipelines and data workflows.
    │   ├── Pipfile               <- The Pipfile for reproducing the pipelines environment
    │   ├── pipelines.py          <- The CLI entry point for all the pipelines
    │   ├── <repo_name>           <- Code for the various steps of the pipelines
    │   │   ├──  __init__.py
    │   │   ├── etl.py            <- Download, generate, and process data
    │   │   ├── visualize.py      <- Create exploratory and results oriented visualizations
    │   │   ├── features.py       <- Turn raw data into features for modeling
    │   │   └── train.py          <- Train and evaluate models
    │   └── tests
    │       ├── fixtures          <- Where to put example inputs and outputs
    │       │   ├── input.json    <- Test input data
    │       │   └── output.json   <- Test output data
    │       └── test_pipelines.py <- Integration tests for the HTTP API
    │
    └── serve                     <- HTTP API for serving predictions
        ├── Dockerfile            <- Dockerfile for HTTP API
        ├── Pipfile               <- The Pipfile for reproducing the serving environment
        ├── app.py                <- The entry point of the HTTP API
        └── tests
            ├── fixtures          <- Where to put example inputs and outputs
            │   ├── input.json    <- Test input data
            │   └── output.json   <- Test output data
            └── test_app.py       <- Integration tests for the HTTP API

[Cookiecutter]: https://github.com/audreyr/cookiecutter
[Pipenv]: https://pipenv.pypa.io/en/latest/
[Prefect]: https://docs.prefect.io/
[Weights and Biases]: https://www.wandb.com/
[MLFlow]: https://mlflow.org/
[FastAPI]: https://fastapi.tiangolo.com/
[asyncio]: https://docs.python.org/3/library/asyncio.html
[ASGI]: https://asgi.readthedocs.io/en/latest/
[uvicorn]: https://www.uvicorn.org/
[Typer]: https://typer.tiangolo.com/
[Pandas]: https://pandas.pydata.org/
[numpy]: https://numpy.org/
[scipy]: https://www.scipy.org/
[seaborn]: https://seaborn.pydata.org/
[jupyterlab]: https://jupyterlab.readthedocs.io/en/stable/
[black]: https://github.com/psf/black
[isort]: https://github.com/timothycrosley/isort
[autoflake]: https://github.com/myint/autoflake
[pylint]: https://www.pylint.org/
[Pytest]: https://docs.pytest.org/en/latest/
[GitHub Pages]: https://pages.github.com/
[Git LFS]: https://git-lfs.github.com/