awesome-readme VS BentoML

About user-statistician

The user-statistician GitHub Action can generate an SVG with a detailed summary of your activity on GitHub. It is mentioned in the tools section of the awesome README awesome list. The SVG it generates includes general information about you (e.g., year you joined, number of followers, number you are following, most starred repository, etc), information about your repositories (e.g., numbers of stars and forks, etc), information about your contributions (e.g., numbers of commits, issues, PRs, etc), and the distribution of languages within your public repositories.

If you really want a stellar README.md take a look at some of the examples in awesome-readme for inspiration!

Awesome README - A collection of high-quality READMEs from a variety of projects, organized by topic. https://github.com/matiassingers/awesome-readme

Provide a succinct and comprehensive README: readers of your personal project will always start with the README to know where to begin. The goal of the README is to provide the reader an understanding of the business problem you are trying to solve, how your solution goes about solving it (solution architecture diagram), and how to get started and run your code. There are plenty of great README examples here: https://github.com/matiassingers/awesome-readme

About user-statistician

Recently, the user-statistician GitHub Action was added to the tools section of Awesome README, which is an Awesome List that includes a curated collection of examples of Awesome READMEs from open source projects, as well as tools enabling creating Awesome READMEs. The Awesome README list is a great place to go if you are looking for ideas for how to improve the READMEs of your open source projects. The Awesome README list covers READMEs more generally, but the tools section includes a few tools focused on Profile READMEs, in addition to many tools for project READMEs more generally. The user-statistician GitHub Action is in the Tools Section.

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there are a few tools you can use as "cheat mode" shortcuts to give you a leg up as you're getting started. here's one: https://github.com/bentoml/BentoML

Then you can look into bentoml https://github.com/bentoml/BentoML which is used to deploy ml stuff with many more benifits.

For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.

In this category I’m a big fan of https://github.com/bentoml/BentoML

What I like about it is their idiomatic developer experience. It reminds me of other Pythonic frameworks like Flask and Django in a good way.

I have no affiliation with them whatsoever, just an admirer.

Github Page: https://github.com/bentoml/BentoML