kilroy-server-py-utils

utilities for kilroy servers in Python 🔧

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This README provides info about the development process.

For more info about the package itself see package README or docs.

Quickstart (on Ubuntu)

$ apt update && apt install curl git python3 python3-pip python3-venv
$ python3 -m pip install pipx && pipx install poetry
$ pipx ensurepath && exec bash
$ curl -sSL https://repo.anaconda.com/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh -o miniconda.sh
$ bash miniconda.sh && exec bash
(base) $ git clone https://github.com/kilroybot/kilroy-server-py-utils
(base) $ cd kilroy_server_py_utils
(base) $ conda env create -f environment.yaml
(base) $ conda activate kilroy-server-py-utils
(kilroy-server-py-utils) $ cd kilroy_server_py_utils
(kilroy-server-py-utils) $ poetry install --sync

Quickerstart

If you just want to try it out and don't care about polluting your environment:

$ python3 -m pip install ./kilroy_server_py_utils

Environment management

We are using conda for environment management (but you can as well use any other tool, e.g. pyenv + venv). The major reason is that conda lets you specify python version and will install that version in the environment. This ensures consistency between different instances (developers, CI, deployment).

The first step is of course to install conda.

To create an environment, run from project root:

conda env create -f environment.yaml

And then activate it by:

conda activate kilroy-server-py-utils

Creating the environment is performed only once, but you need to activate it every time you start a new shell.

If the configuration file environment.yaml changes, you can update the environment by:

conda env update -f environment.yaml

Package management

We are using poetry to manage our package and its dependencies. You need to have it installed outside our environment (I recommend to use pipx for that).

To install the package, you need to cd into kilroy_server_py_utils directory and run:

poetry install --sync

This will download and install all package dependencies (including development ones) and install the package in editable mode into the activated environment.

Editable mode means that you don't have to reinstall the package if you change something in the code. The changes are reflected automatically.

However, you need to install the package again if you change something in its configuration (e.g. add a new dependency). But more on that later.

If it's the first time installing the package, poetry will write specific versions of all packages to poetry.lock file. This file should be committed to the repository, so other people can have the exact same versions of all dependencies. It will work because poetry install checks if poetry.lock file is available and uses it if it is.

Testing

We are using pytest for tests. It's already installed in the environment, because it's a development-time dependency. To start first write the tests and put them in kilroy_server_py_utils/tests.

To execute the tests, cd into kilroy_server_py_utils and run:

poe test

Building docs

We are using mkdocs with material for building the docs. It lets you write the docs in Markdown format and creates a nice webpage for them.

Docs should be placed in kilroy_server_py_utils/docs/docs. They are pretty straightforward to write.

To build and serve the docs, cd into kilroy_server_py_utils and run:

poe docs

It will generate site directory with the webpage source and serve it.

Adding new dependencies

If you need to add a new dependency, look into pyproject.toml file. Add it to tool.poetry.dependencies section. If it is a development-time dependency you need to mark it as optional and add it to the right groups in tool.poetry.extras.

After that update the installation by running from kilroy_server_py_utils directory:

poe update

This will install anything new in your environment and update the poetry.lock file. Other people only need to run poetry install to adjust to the incoming changes in the poetry.lock file.

Continuous Integration

When you push changes to remote, different GitHub Actions run to ensure project consistency. There are defined workflows for:

• deploying docs to GitHub Pages
• testing on different platforms
• drafting release notes
• uploading releases to PyPI

For more info see the files in .github/workflows directory and Actions tab on GitHub.

Generally if you see a red mark next to your commit on GitHub or a failing status on badges in README it means the commit broke something (or workflows themselves are broken).

Releases

Every time you merge a pull request into main, a draft release is automatically updated, adding the pull request to changelog. Changes can be categorized by using labels. You can configure that in .github/release-drafter.yaml file.

Every time you publish a release the package is uploaded to PyPI with version taken from release tag (you should store your PyPI token in PYPI_TOKEN secret).