Show HN: dstack – an open-source tool to build data applications easily

Dear HN,

I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai).

A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser.

Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot more open source data science frameworks than we integrated ourselves. For example, as a user, I want to push a matplotlib plot, a Tensorflow model, a plotly chart, a pandas dataframe, and I expect the presentation layer to fully-support it. Supporting all types of artifacts and providing all the tools to work with them solely seems to be a very challenging task. With this, we open-sourced the framework. Now you can build dstack locally, and run it on your servers, or in a cloud of your choice if that’s needed. More details on the project, how to use it, and the source code of the server can be found at the https://github.com/dstackai/dstack repo. The client packages for Python and R are available at the https://github.com/dstackai/dstack-py and https://github.com/dstackai/dstack-r correspondingly.

What’s next: User callbacks- so that application shows not just pre-calculated visualizations but also can fetch data from a store and process it in real-time. ML models- so that data scientists can publish a stack which binds together a pre-calculated ML model and user parameters Use cases- Support specific use cases that help data scientists to build data science models into data applications as fast as possible.

We would be happy to get your feedback on the open-source framework and also get your opinion on what kind of use cases can be built on top of the framework? Thank you.

Looks great, how do you compare to other alternatives in the same space?

Hi, I'm Peter, a part of the team. There is quite a few solutions already that try to make it easy to make data applications with Python or R. The most relevant solutions include Plotly Dash, Shiny, Voila, Streamlit. All of them are great projects even though all of them are very different. Our project is an attempt to explore this area and figure out what would be a way to build these applications without having a need to do programming, CSS, HTML, or deployment.

Basically, we want to make it possible to make data apps as simple as writing a few lines of code using only the libraries that data scientists already know - pandas, Matplotlib, scikit, Tensor, pytourch, etc. Ideally so you don't have to write your application code at all, and rather deploy your data science models and simply bind them with a simple UI logic. We believe the need to apply ML to enterprise use-cases will grow even more and tools like that will be very useful. Basically you'll be able to create an application that help your HR/Sales/Marketing/Product/ department to apply ML – in minutes, without the need to write this application, deploy or maintain.

streamlit.io is one that i've just finished trying, and find it easy to use.

Does this need some License information adding to your repo? Something that protects you from someone taking this and running their own paid hosted version :) I take it it isn't MIT!