jupyenv VS awesome-jupyter

> There aren't good boundaries between Jupyter's own Python environment, and that of your notebooks— if you have a dependency which conflicts with one of Jupyter's dependencies, then good luck.

I believe that you can use https://github.com/tweag/jupyenv for this.

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

I'm new to Nix and I'm trying to set up a Jupyter notebook environment for Haskell that includes the Hasktorch package. I'm using the jupyenv project from Tweag as the foundation, and I've been able to get it working with some basic Haskell packages. However, I'm running into issues when I try to add Hasktorch to the mix.

i looked at https://nixos.org/manual/nixpkgs/stable/#python https://nix-tutorial.gitlabpages.inria.fr/nix-tutorial/index.html https://github.com/tweag/jupyterWith and i can setup jupyterwith + math-nix + flake to make torch + jupyter

You could look into jupyterWith. With that you can list the packages you want to use in a shell.nix file; based on this file an environment is created in which Jupyter is run. I've also had issues with using packages with regular IHaskell in the past, but jupyterWith works pretty well for me.

(Hopefully the Flakes version of Nix gets released soon, and JupyterWith gets the flake treatment!)

What are some Software Development methods for reducing errors:

1. AUTOMATED TESTS; test assertions

To write spreadsheet tests:

A. Write your own test assertion library for their macro language; write assertEqual() in VBscript and Apps Script.

B. Use another language with a test library and a test runner; e.g. Python and the `assert` keyword, unittest.TestCase().assertEqual() or pytest.

C. Test the spreadsheet GUI with something like AutoHotKey.

From https://news.ycombinator.com/item?id=35896192 :

> The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system.

> awesome-jupyter#testing: https://github.com/markusschanta/awesome-jupyter#testing

> ml-tooling/best-of-jupyter lists papermill/papermill under "Interactive Widgets/Visualization" https://github.com/ml-tooling/best-of-jupyter#interactive-wi...

https://github.com/markusschanta/awesome-jupyter#hosted-note...

In the open source world, look at the sort of stuff you can learn for free...... https://github.com/markusschanta/awesome-jupyter

> Rendering/Publishing/Conversion https://github.com/markusschanta/awesome-jupyter#renderingpu... :

> ContainDS Dashboards - JupyterHub extension to host authenticated scripts or notebooks in any framework (Voilà, Streamlit, Plotly Dash etc)

Streamlit lists Bokeh, Jupyter Voila , Panel, and Plotly Dash as Alternative dashboard approaches: