Cookiecutter Deep Learning Template

Never have problems again when setting up a DL software project.

Purpose

• Simplified deployment of research software and reproducibility of results for other users
• Improve and verify code quality using external tools
• Reduction of redundant tasks at project start
• Best-practices information for the successful development of a DL project

Features

• Supports python3.9+
• poetry for managing dependencies
• mypy for static typing
• pytest and hypothesis for unit tests
• flake8 and wemake-python-styleguide for linting
• docker for development, testing, and production
• sphinx for documentation

Requirements to use the cookiecutter template:

• Python 3.9+
• Cookiecutter 1.7+

Install Cookiecutter Python package with pip

pip install cookiecutter

To start a new project, run:

cookiecutter https://github.com/Ivo-B/CC-DL-template

Cookiecutter prompts you for information regarding your plugin:

project_name [Unique Project Name]:
repo_name [unique-project-name]:
module_name [uniqueprojectname]:
author_name [Your name]:
author_mail [Your.name@email.com]:
description [A short description of the project.]:
Select dl_framework:
1 - Tensorflow
2 - PyTorch
Choose from 1, 2, 3 [1]:
Select add_example_code:
1 - yes
2 - no
Choose from 1, 2 [1]:
Select license:
1 - MIT
2 - BSD-3
3 - Apache-2.0
4 - GNU GPL v3.0
5 - Mozilla Public License 2.0
6 - none
Choose from 1, 2, 3, 4, 5, 6 [1]:

License

MIT. See LICENSE for more details.

Based on:

cookiecutter-data-science

wemake-django-template

Deep-Learning-In-Production