minimon 0.1.1

minimon

Minimon

This is just a stub for a future Readme file for minimon, the minimal monitor.

Here are some buzz-phrases:

• Minimal monitoring
• textual/rich based
• Heavy use of async programming
• asyncssh instead of paramiko
• Configuration as application
• Functional approaches wherever possible

Installation

While you can just clone and use minimon, the intended way to use it is to install via pip or run it from inside a virtual environment.

Install it locally using pip:

[<PYTHON> -m] pip[3] install [--user] [--upgrade] minimon

Why

• async
• functional
• testability
• bullet proof

Todo

This is very early development, no real todo-list here, yet. Some of the bigger tasks include

• provide a way to use data propagating through functions by different consumers
• provide broader set of useful data sources and handlers
• provide monadic (async) function chaining
• capture and persist metrics
• visualize metrics
• provide ways to interact
• improve logging: to file, log threads, log task context
• and error handling

Development & Contribution

Setup

For active development you need to have poetry and pre-commit installed

python3 -m pip install --upgrade --user poetry pre-commit
git clone [e-mail]:frans/minimon.git
cd minimon
pre-commit install
# if you need a specific version of Python inside your dev environment
poetry env use ~/.pyenv/versions/3.10.4/bin/python3
poetry install

Workflow

• Create/test/commit changes and check commits via pre-commit
• after work is done locally:
  • adapt version in pyproject.toml
  • build and check a package

poetry build && \
twine check dist/* &&
python3 -m pip uninstall -y minimon && \
python3 -m pip install --user dist/minimon-$(grep -E "^version.?=" pyproject.toml | cut -d '"' -f 2)-py3-none-any.whl

• check installed package
• go through review process
• publish the new package poetry publish --build
• commit new version && push

License

For all code contained in this repository the rules of GPLv3 apply unless otherwise noted. That means that you can do what you want with the source code as long as you make the files with their original copyright notice and all modifications available.

See GNU / GPLv3 for details.

This project is not free for machine learning. If you're using any content of this repository to train any sort of machine learned model (e.g. LLMs), you agree to make the whole model trained with this repository and all data needed to train (i.e. reproduce) the model publicly and freely available (i.e. free of charge and with no obligation to register to any service) and make sure to inform the author (me, [e-mail]) via email how to get and use that model and any sources needed to train it.