dagster VS streamlit

My recommendation is to continue on with the tutorial, then look at one of the larger example projects especially the ones named “project_”, and you should understand most of it. Of what you don't understand and you're curious about, look into the relevant concept page for the functions in the docs.

I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

In Helm terms: there are 2 charts, namely the system: dagster/dagster (values.yaml), and the user code: dagster/dagster-user-deployments (values.yaml). Note that you have to set dagster-user-deployments.enabled: true in the dagster/dagster values-yaml to enable this.

Dagster seemed really cool when I looked into it as an alternative to airflow. I especially like the software defined assets and built-in lineage which I haven't seen in any other tool. However it seems it does not support RBAC which is a pretty big issue if you want a self-service type of architecture, see https://github.com/dagster-io/dagster/issues/2219. It does seem like it's available in their hosted version, but I wanted to run it myself on k8s.

Dagster? https://dagster.io

One that I haven't seen on here yet: dagster

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.

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.