cookiecutter-data-science | reasonably standardized , but flexible project structure

by drivendata Python Version: v1 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | cookiecutter-data-science Summary

cookiecutter-data-science is a Python library typically used in Template Engine applications. cookiecutter-data-science has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

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Quality

Security

License

Reuse

Support

cookiecutter-data-science has a medium active ecosystem.

It has 6773 star(s) with 2168 fork(s). There are 122 watchers for this library.

It had no major release in the last 12 months.

There are 41 open issues and 96 have been closed. On average issues are closed in 188 days. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cookiecutter-data-science is v1

Quality

cookiecutter-data-science has 0 bugs and 0 code smells.

Security

cookiecutter-data-science has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cookiecutter-data-science code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cookiecutter-data-science is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cookiecutter-data-science releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 216 lines of code, 15 functions and 17 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed cookiecutter-data-science and discovered the below as its top functions. This is intended to give you an instant insight into cookiecutter-data-science implemented functionality, and help decide if they suit your requirements.

Display a deprecation warning .
Generate final data set from raw data .

Get all kandi verified functions for this library.

cookiecutter-data-science Key Features

No Key Features are available at this moment for cookiecutter-data-science.

cookiecutter-data-science Examples and Code Snippets

Cookiecutter Science Project,Organization and additional features

Python

Lines of Code : 42

License : Permissive (MIT)

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├── setup.py
├── README.md             <- The top-level README for developers using this project.
├── LICENSE
├── environment.yml       <- Conda environment file. Create environment with
│                           `conda env create -f environm

Example of a reproducible machine learning model,Repo structure

Python

Lines of Code : 42

License : No License

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├── README.md                         <- You are here
│
├── config                            <- Directory for yaml configuration files for model training, scoring, etc
│   ├── logging/                      <- Configuration of python loggers

Project Organization

Shell

Lines of Code : 29

License : Permissive (MIT)

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├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make deploy`
├── README.md          <- The top-level README for developers using this project.
├── data
│   │
│   └── raw            <- The original,

Community Discussions

Trending Discussions on cookiecutter-data-science

How to separate development and production requirements.txt for Machine Learning Project?

QUESTION

How to separate development and production requirements.txt for Machine Learning Project?

Asked 2021-Oct-12 at 03:47

I'm looking for a better AI/ML project code structure. I know that cookiecutter is there and I really like it.

Here is the problem: I want my Jupyter Notebook added to the project structure like cookiecutter. But when I want to deploy the model and I pip install requirements.txt, all of the package (including Jupyter Notebook requirements) will be installed. I didn't like it.

Is there any project structure, that include notebook inside but separate requirements.txt for analysis and deployment?

Is it good idea to create two folder: one for analysis on notebook with requirements.txt and one for model deployment with its own requirements.txt?

...

ANSWER

Answered 2021-Oct-11 at 19:35

The best solution that comes to my mind is Poetry. It automatically creates the folder structure like a python package.

Folder structure

Poerty creates a project.toml file when project is initialized. This can serve as requirement.txt file for production.You can add production and development package separately in this file using command line or editing the file.

Project.toml

It also creates separate environment for the project which helps in minimizing the conflict with other project.

Source https://stackoverflow.com/questions/69526483

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cookiecutter-data-science

You can download it from GitHub.
You can use cookiecutter-data-science like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.