voila VS dockerized-jupyter-notebook

Quick link: https://github.com/voila-dashboards/voila

Humbly recommend when you share a product, you include a link to it ;)

https://voila.readthedocs.io/en/stable/

> Works with CI/CD out of the box. Deploy to vercel, netlify, your own infra.

Jupyter is suited for whatever you want to do with it. Voila exists to enable the use case of re-generating notebooks on a CI/CD system: https://github.com/voila-dashboards/voila

Anyways, seems like the templating is more powerful than the one being offered by Jupyter Notebooks.

Good luck and much success with it :)

i don't understand why everyone isn't just using voila. it's so much better than streamlit or gradio. but that's just my opinion i guess.

Ill have to check it out and see how it compares to voilà and holoviz panel. What I like about Holoviz panel is you can create a data web app from code that resides in a notebook or create a completely standalone app from just plain py scripts, and it supports many different visualization backends. I have found it to be the more flexible and generalizable data web app framework among the others I have come across (like Voilà, Dash, Plotly, and Streamlit).

Any insights what the differences between this and Voila are? https://github.com/voila-dashboards/voila

A nifty little alternative to voila, one might say.

Markdown and/or Voilà https://github.com/voila-dashboards/voila

If you are comfortable working in a Jupyter Notebook you can combine ipywidgets & Voila.

your task is very very broad

you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))

that would cover a lot of use cases right away without next to no learning curve

If you don't want to deal with AWS or similar, in that case:

- if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)

- if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/

- otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )

there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing