Loguru VS typer

Because you get to see the simultaneous mix of arguments about objective verifiable facts and arguments about yelling at each other. Plus I don't understand how to cook but I do understand Delgan/loguru#563.

How can we integrate with current logging libraries such as logging, logges, loguru? And how would you compare your library with ic

I found loguru online and was thinking if it's relevant for my code. As far as I understand it would be preferable as opposed to me printing my exceptions with print.

You should try the loguru library. I was able to roll a rolling-upload-to-s3 adapter in under an hour. Switching to json logs is one bool flag away. Plus it's gorgeous

https://github.com/Delgan/loguru

Also iirc s3's "file-like interface" does not actually obey the file protocol, which is obnoxious.

If you have a choice, save yourself some heartache and install loguru

This? First time to hear about it. Thanks!

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

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/