BentoML VS streamlit

Link to GitHub -->

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

Developed using Langchain and Streamlit technologies for enhanced performance.

Hello,

Have you ever seen the https://streamlit.io/ ? I think this is what you are looking for.

While building dashboards in Streamlit, I found myself really missing Buefy's (Bulma) modern web components.

Specially due to the inability to add new values to Streamlit's multiselect [1], some missing controls like a polished image carousel [2] or a highly customizable data table.

Long story short, we put together streamfy (Streamlit + Buefy) as an MIT licensed project in GitHub to bring Buefy to Streamlit.

Demo: https://streamfy.streamlit.app

All the form components are implemented, missing half of other non-form UX components. There is plenty of room for PRs, testing, feedback, documentation, example, etc.

Please send issues and contributions to GitHub project [3] and general feedback to X / Twitter [4]

Thanks!

[1] https://github.com/streamlit/streamlit/issues/5348

Looks cool. How do you see this differing from streamlit? https://streamlit.io/

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

In this tutorial, you'll construct a fully functional Streamlit application from the ground up. Streamlit lets you turn simple data scripts into web applications without traditional front-end tools. This application will be capable of downloading audio from any YouTube video, transcribing it using Deepgram, and then summarizing the content with the assistance of Mistral 7B, all streamlined through the capabilities of Langchain.

Install the Stable Diffusion tools and checkpoints, and run it all with Streamlit.

Streamlit is an open-source Python library which makes it easy to build web applications for machine learning and data science. It has a set of rich APIs for visual components including several chat elements, making it quite convenient to build conversational agents or chatbots, especially when combined with LLMs (Large Language Models).

Having successfully deployed your model endpoint, it is now time to show the world how it works, to achieve this we build a web-interface for anyone to use. Enter Streamlit, think of it as a bridge between your data and the world, letting you present insights, perform analyses, and even collect user input with ease. It's perfect for data scientists, analysts, and anyone who wants to leverage the power of their data in a visually appealing and interactive way.