fastapi VS BentoML

only having 1 maintainer

From the official docs:

To save time here, I have prepared a basic Python application developed on FastAPI and if you want to follow along you can get the code from GitHub Repository here.

This repository contains code for asynchronous example api using the Fast Api framework ,Uvicorn server and Postgres Database to perform crud operations on notes.

That sounds like the perfect job for our open-source lib NiceGUI. It is an UI framework on top of FastAPI. That means you can easily setup HTTPS POST endpoints to save the incoming data to a file or database. With very little effort you can also create your webpage which reads the data and displays it.

This package extends the FastAPI response model schema allowing you to have a common response wrapper via a fastapi.routing.APIRoute.

fully type-annotated options for building an API are available - one popular option is FastAPI and everything you expose is declared through pydantic. There is nothing as productive in Go I'm afraid.

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

Elastic License 2: https://github.com/bentoml/bentoctl/blob/v0.3.1/LICENSE.md which also applies to their Yatai kubernetes thing, but strangely not (yet?) to the similarly named repo which is Apache-2: https://github.com/bentoml/BentoML/blob/main/LICENSE

Using a general-purpose framework such as FastAPI involves writing a lot of boilerplate code just to get your API endpoint up and running. If deploying a model for a demo is the only thing you are interested in and you do not mind losing some flexibility, you might want to use a specialized serving framework instead. One example is BentoML, which will allow you to get an optimized serving endpoint for your model up and running much faster and with less overhead than a generic web framework. Framework-specific serving solutions such as Tensorflow Serving and TorchServe typically offer optimized performance but can only be used to serve models trained using Tensorflow or PyTorch, respectively.

BentoML - BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Also mlflow is not that optimized because it doesnt microbatch like torchserve/tfserving/bentoml. https://github.com/bentoml/BentoML/tree/master/benchmark

BentoML.ai | ML Engineer, Backend Engineer | Full-time | Bay Area or Remote | Python, Kubernetes, MLOps platform, Data Infra, Tensorflow, PyTorch, etc

BentoML is an open-source framework for machine learning model serving & deployment https://github.com/bentoml/BentoML

We are a venture backed startup behind the BentoML open source project, and we are looking for engineers who are passionate about building Open Source, MLOps, ML Platform or developer tools. Email [ chaoyu at bentoml.ai] if you are interested.

Job descriptions: https://angel.co/company/bentoml