README.md

Cloud Course: Python Package Template

This is a project scaffold to help you quickly get up and running with a Python package using modern, industry standard tools and practices.

The template comes with an opinionated VS Code settings configuration file to help ensure the best integration with the tools here, e.g.

• autocompletion
• syntax highlighting
• auto-formatting on save
• red/yellow squiggly lines under linting errors
• automatic discovery of tests so they are executable in the VS Code test explorer

Quick start

# install cookiecutter into an isolated virtual environment
python -m venv ./venv/
source ./venv/bin/activate
pip install --upgrade pip
pip install cookiecutter

# cookie cut the template, answering the prompts, e.g.
#  - repo_name: my-awesome-package
#  - package_import_name: my_awesome_package
cookiecutter https://github.com/mlops-club/cloud-course-python-package-cookiecutter.git

What are the opinions in this template?

Notable comments about the file structure

• src/<package-name>/ - contains the package code. Using an src/ folder is a best practice.
• tests/ has minimal boilerplate that
  • Makes fixtures work using conftest.py
  • Makes imports such as from tests.my_file import thing (via some light PYTHONPATH hacking, adding tests/.. to the PYTHONPATH)
  • Contains a unit_test/ and functional_test/ directory whose structure is meant to mirror that of src/<package>/
• .vscode/
  • contains extensions.json which can be used to install several recommended extensions to get a great development experience
  • contains settings.json which configures the various VS Code extensions, e.g. black to read their settings from pyproject.toml

Tools

This project template makes use of

• setuptools for packaging; a pip + venv + setuptools workflow is encouraged as it is considered the most officially supported, "vanilla" way to manage Python packages.

• pylint and flake8 for linting. ruff is up and coming, but does not yet support all the plugins and rulesets that these two do. However, we love ruff and are excitedly watching its progress.

  • flake8 supports plugins, and we are using the following

    • flake8-docstrings - ensure that functions have docstrings

    • flake8-pyproject - necessary for flake8 to be configured using a pyproject.toml file

    • radon - a standalone tool for measuring cyclomatic complexity.

      Radon out-of-the-box is a flake8 plugin which enables VS Code and PyCharm to display red squiggly lines on functions whose complexity score is too high.

• black for formatting. Note that the line length is set to 119 for black and other tools that consider line length.

• pytest for unit testing since it is the industry standard testing framework

  • pytest-cov for calculating coverage and displaying an HTML coverage report in the browser

• pre-commit framework - a convenient, industry-standard way to install and run flake8, pylint, and the other tools all at once--and in isolated virtual environments. See .pre-commit-config.yaml for the configuration.

• Makefile - for running tasks, e.g. make lint, make test, make install.

  Make is unfortuanately a tool that comes pre-installed on most OSes, but it comes with its own scripting language which is limited and difficult to use compared to bash. It supports tab-based autocompletion when running commands and can be used to chain commands together, e.g. make lint test build.

  This template uses Makefile as a paper-thin wrapper over run.sh which allows you to author your tasks in bash, while still providing the convenience of tab-based autocompletion and chaining commands together.

  We are not in love with Makefile + run.sh, but it has advantages over alternative workflows such as

  • bash scripts are flexible. Most developers can quickly author simple bash scripts, especially with ChatGPT.
  • Justfile is awesome, but can be difficult to install in CI and most devs do not already have it installed.
  • PyInvoke is a thing, but requires an initial installation of invoke with Python, resulting in extra steps, e.g. installing in the system Python, or with pipx. It also has a slight learning curve over the first adding that much more friction to new developers joining projects.

  In summary, Makefile + run.sh is a decent solution for minimized learning curve (uses ubiquitous tools), easy setup, and performance (no slight hang when executing Python-based tasks).

Notes

• mypy could be added but is not present in this template

I need help learning about all of these tools

For an in-depth, step-by-step guide on the history of Python packaging and explanations of all the tools used in this scaffold, you can take the Udemy course Taking Python to Production: A Professional Onboarding Guide where we develop this template from scratch as well as many other foundational SWE skills, e.g. git, GitHub, CI/CD GitHub Actions, zsh, and testing.

Contributing

Roadmap

• Add screenshots and GIFs to the README of the cookie cut project that demonstrate the VS Code integrations, e.g. the Test Explorer, squiggly lines, autoformatting, etc.