3. Configuration#
As described in the Introduction section, the control center is a powerful DevOps tool that facilitates configuration management by enabling CCA and rendering your whole project highly dynamic. For most cases, it is the only interface you need to interact with to configure, customize, and manage your entire project. This interface is a directory in your repository, containing YAML configuration files as well as optional Python modules for advanced customizations. They contain all metadata, settings, and variables for your project and its workflows, organized in a structured format. PyPackIT uses these configuration files to automatically maintain your project in various ways:
Instead of manually configuring tools and platforms using different user interface, PyPackIT automatically configures them via APIs. Examples include various GitHub repository settings, e.g., project metadata, branch/tag names and protection rules, issue labels, GitHub Pages settings.
Instead of creating and maintaining different configuration files for tools and services, PyPackIT automatically generates and updates them. Examples include Python package configuration files (e.g.,
pyproject.toml, conda recipes), GitHub configuration files (e.g., issue and discussion forms, pull request templates, code owners), and environment files (e.g., piprequirements.txt, condaenvironment.yaml).License, citation, and various documentation files like READMEs and community health files are all automatically generated and maintained from simple inputs. For example, PyPackIT can generate license files from any SPDX license expression, with support for multiple Markdown formats.
Any other file can be dynamically generated or augmented. Examples include injecting docstrings, comments, or any arbitrary piece of code into Python source files. For example, by default the project’s license and copyright information are added as comments to the top of all modules, and the main module’s docstring is automatically generated from project descriptors.
All project workflows and pipelines are fully customizable, with more than four thousand available options. These include team configurations for automatic task assignment and governance, issue management and version control settings, configurations for Continuous integration and deployment pipelines, metadata for indexing repositories, and many more.
Therefore, the control center configurations act as a single comprehensive source of data from which most of your project can be automatically generated and maintained. In fact, since the control center is able to dynamically generate any file, it is possible to include your entire project (with all Python codes, test cases, and documentation) in the control center as (static or dynamic) configurations, so that everything can be dynamically updated. Changes to control center configurations are automatically propagated throughout your repository and all supported tools and services during so-called synchronization events that trigger the CCA pipeline. These are carried out automatically on GHA, but can also be invoked locally using PyPackIT’s CLI.
Learn more about the location of your project’s control center directory and how to customize it.
An overview of the general structure and content of your project’s control center directory, and ways you can customize them.
A summary of the available control center options and their default values, as well as instructions on adding additional custom configurations.
Instructions on how to use templating in your repository’s control center files to dynamically generate and reuse data.
Learn how to dynamically inherit specific configurations from online resources and simultaneously manage multiple projects.
Instructions on how to add custom plugins that can hook into specific workflow steps to extend PyPackIT’s functionalities.
Overview of PyPackIT’s Continuous Configuration Automation pipeline and how it renders your entire project highly customizable and dynamic.
A summary of all dynamic files and directories that are automatically generated and maintained by PyPackIT’s control center.
Instructions on how to configure caching behaviour in PyPackIT to speed up your workflows and reduce web API usage.