cado VS mercury

I'm running a SaaS for serving Python notebooks as web apps [0]. We offer widgets for notebooks and server, both as open source [1]. In open source you are managing the server instance with default Django Admin Panel. In the SaaS version, we have a dashboard for managing site (adding users, setting visibility, usage analytics), the dashboard is closed source. The open source version by default is single site, but can be switched to multi-tenant (multiple domains and subdomains) just by adding instances in the database. In case of update, sometimes it is required to update both code bases. Employees have access to both code bases.

We started with open-source first, and added SaaS offering after ~2 years. The code base split was a natural choice. At first, I didn't want to add SaaS, because managing servers is a lot of work. But, we have a lot of requests for such service, and it makes really easy to deploy notebook online (with few clicks you have unique domain and notebook running). I'm happy with this code base split.

[0]: https://runmercury.com

[1]: https://github.com/mljar/mercury

I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.

My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/

disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury

Web App is using two open-source packages that I've created:

- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised

- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury

You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.

The GitHub repository https://github.com/mljar/mercury

We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.

It is very opinionated:

- it has no callbacks - we automatically re-execute cells below updated widget

- it has no layout widgets, all input widgets are always in the left sidebar

Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.

The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.

The GitHub repository https://github.com/mljar/mercury

Documentation https://RunMercury.com/docs/

Mercury Cloud https://cloud.runmercury.com

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.