jupyenv VS nb_conda_kernels

> 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!)

If you use conda there are extensions that can help with this by automatically registering any available conda environments that include ipykernel in your Jupyter Lab environment.

nb_conda_kernels is pretty reliable but not actively maintained. Gator from the mamba folks is new and still a bit rough around the edges but looks like it will be pretty slick eventually.

https://github.com/Anaconda-Platform/nb_conda_kernels

https://github.com/mamba-org/gator

it sounds like what you're trying to do is what https://github.com/Anaconda-Platform/nb_conda_kernels does, except with pipenv. I unfortunately do not have a solution for you for pipenv, but nb_conda_kernels does seamlessly allow you to launch kernels in environments other than the environment Jupyter is running in without having to start multiple instances of jupyter.