Inspired by the cookiecutter-data-science template. This template has been updated to better fit machine learning-based projects and is being used as the core template in this MLOps course.

• Python 3.11
• Cookiecutter v2.4.0

Start by creating a repository either using the Github GUI in the webbrowser or alternatively you can use the Github command line interface if you have set it up:

gh repo create <repo_name> --public --confirm

Afterwards on your local machine run

cookiecutter https://github.com/SkafteNicki/mlops_template

and input starting values for the project. When asked for the repository name when creating the template, input the same name as when you created the repository. Note that when asked for the project name, you should input a valid Python package name. This means that the name should be all lowercase and only contain letters, numbers and underscores. The project name will be used as the name of the Python package. This will automatically be validated by the template.

To commit to the remote repository afterwards execute the following set of commands:

cd <repo_name>
git init
git add .
git commit -m "init cookiecutter project"
git remote add origin https://github.com/<username>/<repo_name>
git push origin master

🐍 Python projects using pyproject.toml

🔥 Models in pytorch

📦 Containerized using docker

📄 Documentation in mkdocs

👕 Linting and formatting with ruff

✅ Checking using pre-commit

🛠️ CI with Github actions