robusta VS typer

To put one more option out there, we use Hikaru (https://pypi.org/project/hikaru/) in Robusta.dev (https://github.com/robusta-dev/robusta) and have been pretty happy with it. Example code below:

  with Pod().read(name='thename', namespace='the-namespace') as p:

Open Source alternative based on Prometheus

I'm from the Robusta team (https://github.com/robusta-dev/robusta) which bursts upwards towards the end. Happy to answer any questions!

The guys at Robusta, added it into thier slack so when alerts pop of issues in k8s, chatgpt can be used to investigate and offer options to check and potentially solutions. Next phase is probably to let it also apply it :)

It's no replacement for learning how things work, but we're trying to automate common troubleshooting cases with Robusta. https://github.com/robusta-dev/robusta

https://docs.robusta.dev/master/ https://github.com/robusta-dev/robusta

This is the sort of thing we built Robusta for: https://github.com/robusta-dev/robusta

I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.

https://typer.tiangolo.com/

Adding CLI capabilities. My preferred library here is typer.

I just heard about this but it seems to be pretty much the type of thing you want and want fast.

Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.

Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).

So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.

Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.

[1] https://typer.tiangolo.com/

interesting that you hate python, but love Click. Did you try Typer which uses Click underneath?