voila VS jupyter-book

https://data-apis.org/dataframe-protocol/latest/purpose_and_...

Practically, pd.DataFrame(,dtype_backend="arrow") may be a quick performance boost.

Jupyter kernels with Papermill or cron and templated parameters at the top of a notebook with a dated filename in a (repo2docker compatible) git repo also solve for interactive reports. To provision a temporary container for a user editing report notebooks, there's Voila on binderhub / jupyterhub, https://github.com/binder-examples/voila

or repo2jupyterlite in WASM with MathTex and Pyodide's NumPy/pandas/sympy: https://github.com/jupyterlite/repo2jupyterlite

But that's not a GUI, that's notebooks. For Jupyter integration, TIL pyqtgraph has jupyter_rfb, Remote Frame Buffer: https://github.com/vispy/jupyter_rfb

repo2docker https://github.com/binder-examples/voila #dashboards #ContainDS

Sphinx supports ReStructuredText and Markdown.

MyST-Markdown supports MathJaX and Sphinx roles and directives. https://myst-parser.readthedocs.io/en/latest/

jupyter-book supports ReStructuredText, Jupyter Notebooks, and MyST-Markdown documents:

You can build Sphinx and Jupyter-Book projects with the ReadTheDocs container, which already has LaTeX installed: https://github.com/executablebooks/jupyter-book/issues/991

myst-templates/plain_latex_book:

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

Note: If you want to present a cleaner version of the notebook without assertions, you can use Jupyter book to render it into a site and use the remove-cell tag to omit assertions from the output.

See this thread for example.

And maybe take a look at executablebooks/jupyter-book.

Our team has transferred from Sphinx for documentation to JupyterBook. There have been some growing pains with it but I prefer the look of the output and being able to play with the examples on Colab or Binder at the click of a button is a great feature.