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https://github.com/NeowayLabs/data-science-template
A template for Data Science projects with a solid Software Engineering Architecture
https://github.com/NeowayLabs/data-science-template
datascience model project template
Last synced: about 2 months ago
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A template for Data Science projects with a solid Software Engineering Architecture
- Host: GitHub
- URL: https://github.com/NeowayLabs/data-science-template
- Owner: NeowayLabs
- License: mit
- Created: 2019-05-30T12:50:14.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-10-07T19:46:04.000Z (over 5 years ago)
- Last Synced: 2024-08-09T02:18:11.653Z (6 months ago)
- Topics: datascience, model, project, template
- Language: Makefile
- Homepage:
- Size: 30.3 KB
- Stars: 44
- Watchers: 15
- Forks: 11
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - NeowayLabs/data-science-template - A template for Data Science projects with a solid Software Engineering Architecture (Makefile)
README
# Data Science Template
[](https://travis-ci.org/NeowayLabs/data-science-template)
This repository contains a project creation tool for Data Science
models. The main usage is quite simple, but we have a lot of features
working **OUT OF THE BOX**:
+ `Dockerfile` template;
+ Unit-tests architecture with coverage report;
+ Makefile for project management and Docker abstraction;
+ Python software distribution via `setup.py`;
+ Multiple releases formats into `/dist`: `.tar.gz`, `.egg` and `.whl`;
+ Documentation: Project Specification && Model Report;
+ Notebook template for data analysis;
+ Multiple linters to help catching bugs during development:
+ security: **bandit**;
+ style: **flake8**;
+ typing: **mypy**;
+ CI/CD configuration with 3 stages: test, build and pages
+ tests: run unit-tests;
+ build: build docker image and populate `/dist`;
+ pages: build html documentation using
[Sphinx](http://www.sphinx-doc.org/en/master/) and publish using
[Read The Docs](https://readthedocs.org/);
# Usage
So let's create a new model! You just need clone this repository and
use the bash script `create-model` as follow:
``` bash
git clone [email protected]:NeowayLabs/data-science-template.git
cd data_science_template
./create-model ~/awesome-model
```
The basic placeholders like `project name`, `python package name` are
automatically filled! If you have configured the git `user.name` and
`git user.email` the system will assume to be equal as `author` and
`email` and will replace these placeholders as well. The directory
created will be initialized as a git repository, but you still need
configure your remote url manually.
The `create-model` program will help you configure the remote printing
some instructions to do that correctly, keep **attention** to blue
exclamation marks `[!]` on output of `create-model`. But basically you
need do two steps:
1. Create a new project in [GitHub](https://github.com/).
2. Get the ssh or https remote from it.
You can test the remote address executing a git clone command:
```bash
git clone
```
To enable properly CI/CD functionality for your new repository you can
do the following:
1. Go to [Travis CI](https://travis-ci.org/).
2. Select your repository to use with Travis CI.
[These instructions](https://docs.travis-ci.com/user/tutorial/#to-get-started-with-travis-ci)
may help.
After this, update your git remote `origin` and do the
first commit:
``` bash
cd ~/awesome-model
make check # check if everything is ok and tests are passing
git add .
git commit -m "Initial commit" # Or your desired message
git remote add origin
git push -u origin master
```
# Releasing Software
You can release a new version of your software with this command:
```bash
make release VERSION=X.Y.Z
```
That is all. That command will build the docker image, run the tests,
linters and if everything is ok: a new git tag will be created and
be pushed to GitHub.
# Author
Neoway @ Data Analytics Team