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.
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.
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?