spark-rapids VS streamlit

His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.

The spark-rapids project allows one to run multi-GPU ETL workloads on a Spark cluster. https://github.com/NVIDIA/spark-rapids In such a setup, the GPU nodes are part of the Spark cluster. Multi-GPU nodes are viable, although an executor is currently limited to a single GPU.

So, in my day job at NVIDIA, I work on the RAPIDS Accelerator for Apache Spark, which is an open-source plugin that provides GPU-acceleration for ETL workloads, leveraging the RAPIDS cuDF GPU DataFrame library.

Streamlit (https://streamlit.io/)

Does it need to be live (i.e when database or underlying spreadsheet updates does it need to be reflected in real time on the dashboard) or are you ok with static display.

Live updating data is a pain I've messed around using javascript to force refresh html iframes on a timer. But I was never really satisfied with this. I've heard you can do things with websockets but that is starting to get too complicated for me (I'm not a programmer).

For static stuff one of the data scientists in my org pointed me to Streamlit (https://streamlit.io/) it's a python package I found very easy to use. Can easily combine SQL with CSV imports and display them all on one dashboard. Can use forms toggle butotns etc to control the display.

Note that there are many tools that make this easier/simpler to prototype, including chainlit, streamlit, etc… The backend API we built is amenable to interacting with them as well.

2.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.

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

Here's your savior, let's welcome Streamlit.

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.