The ansible-jupyter-kernel repository provides a Jupyter Notebook kernel that enables users to run Ansible tasks and playbooks directly within interactive notebook cells. This integration bridges the gap between Jupyter's interactive computing environment and Ansible's infrastructure automation capabilities, allowing users to construct and execute Ansible plays on the fly without needing to manage separate playbook files and configuration directories.
The kernel is written in Python and can be installed from PyPI or from a local checkout. Installation is straightforward, with the setup package automatically registering the kernel with Jupyter. The project supports multiple installation methods including standard pip installation, local development installation with the -e flag, and Anaconda/Miniconda environments using the --sys-prefix option. For users preferring containerized environments, a Docker image is available at benthomasson/ansible-jupyter-kernel that runs the kernel with Jupyter pre-configured and accessible via browser.
The core functionality revolves around cell-type prefixes that allow users to structure Ansible components within notebook cells. Users denote cell contents with a hash symbol followed by the type designation. Supported cell types include inventory for defining target hosts, play for opening Ansible play blocks, task as the default cell type for Ansible tasks, host_vars for host-specific variables, group_vars for group-specific variables, vars for custom variable files, template for creating templated files, and ansible.cfg for configuration overrides. This cell-based structure replicates the modular nature of traditional Ansible playbooks while leveraging Jupyter's interactive execution model.
The repository includes example notebooks demonstrating various use cases and workflows. These examples serve as reference implementations for users learning to work with the kernel. The project supports development workflows using pipenv, providing a standardized development environment for contributors.
The project is classified across multiple domains reflecting its dual nature as both a development tool and infrastructure automation platform. These classifications include development environment, Jupyter kernel, notebook environment, Ansible integration, infrastructure management, automation, DevOps, and interactive computing. The tool targets users who work at the intersection of data science workflows and infrastructure automation, enabling them to leverage Jupyter's interactive capabilities for Ansible-based infrastructure tasks.
