Tenacity VS magenta

tenacity – Retrying library for Python

Today, I'm going to take you on a thrilling coding adventure inspired by a LinkedIn code snippet, where I tangled with FastAPI, River, Watchdog, and Tenacity. Ready? Buckle up!

This depends on the criticality of the task. If it's not worth adding persistence you can have a look at https://github.com/jd/tenacity , it's a flexible retry decorator, it does not require too much effort to use. If you need persistence without introducing too much development of your own maybe have a look at celery or dramatiq as suggested, but I didn't use celery ever since I left django, didn't try dramatiq either.

To implement the back-off, I recommend the tenacity lib. You can customize the retrying settings easily

Hi all. After more then half a year study advanced python and SE principles I would like to develop a library of my liking. I’m aiming for a common decorators library. But after I saw the source code of tenacity (A retry pattern library). I was discouraged. It’s still very complicated to me for now. I can understand a lot of tenacity now. But to code it as beautiful as the auther is tough. Tenacity is not even a big library. My goal is to develop something bigger. I read Learning Python, know many standard library, then two books on SE. A design patterns book for python (broad not deep). And a clean code book (This one is pretty deep). Beyond that I know data structure and algo stuff. I thought I know python at an immediate level now. But seems source code is out of reach. Just wondering how long in python dev before you can develop something like tenacity.

Not sure why you have that impression. The majority of production code I've touched, from networking to distributed systems, implement retry logic. While many have in-house code, developed before retry libraries became available or working within environments unable to pull much external code, the majority of newer code I hear about uses [Tenacity](https://github.com/jd/tenacity).

You're probably looking for something like this: https://github.com/jd/tenacity

Several retry libraries exist. For example, tenacity.

Paper : https://arxiv.org/abs/1705.06830v2 Code : https://github.com/magenta/magenta/blob/main/magenta/models/arbitrary_image_stylization/README.md

I'm interested in retraining https://github.com/magenta/magenta/tree/main/magenta/models/arbitrary_image_stylization from scratch in order to test it on higher resolutions. Unfortunately it seems that some of the training sets are no longer available (Kaggle painters for instance).

The problem with FFTs is that for the lower frequencies you have very few bins, but at the higher end you get ridiculous accuracy and there is no easy way to make this more linear. Binning on the high end saves some space but doesn't make the low any more accurate.

So you need to run multiple methods in parallel and decide based on the very rough distribution of the energy in the spectrum which method has the biggest chance of success, or, alternatively, to use the output of both methods to drive some logic that will assign a weight to the output of each.

It's a tricky problem, to put it mildly. Also, this is the simplest form of the problem, doing this accurately for multiple pitches at once is much harder.

Another source of inspiration is the 'onsets and frames' software that powers some automated transcription software:

https://github.com/magenta/magenta/tree/master/magenta/model...

I think if this code is over your head that maybe a good introduction course on signal processing would be a nice thing to have under your belt.

Best of luck!

As mentioned in other comments, doing a project would help to take the next step, especially something that'd help you personally. You might be able to do something related to music as well, see https://github.com/vinta/awesome-python#audio for some modules. There's https://github.com/magenta/magenta for generating art/music. Specializing in a niche area could help if you decide do freelancing, write books, etc.

check out https://github.com/magenta/magenta (found it via https://github.com/vinta/awesome-python#miscellaneous)