On Monday, August 3, 2020 at 9:26:38 PM UTC-7, Tim McLarnan wrote:
>
> I tried copying the kernel folders from CoCalc and editing them in what I 
> thought was the right way and putting them with the kernel folders on my 
> machine.  This produced kernels that show up in Jupyter, but that don't 
> work.  No doubt someone who knows the right things to put in the .json 
> files could make this work, but I can't.  This has to be an absurdly 
> complicated approach, though.  Surely there's just some trivial thing I 
> don't know about.
>

That got you the configuration of the kernels, but likely not the actual 
code that implements them. Jupyter kernels are basically stand-alone 
programs. Often, they are written in python (but they don't have to be!) 
and installing them consists of two steps: installing the software and 
informing the jupyter notebook installation of their presence.

For kernels written in python, you end up needing to install python 
software, for which there are excellent tools.

A very simple example, that I happen to have written myself, so that's why 
I'm familiar with it, but needs "magma" present on your machine to work.

https://github.com/nbruin/magma_kernel

The instructions indeed show two installation steps: one for installing the 
python package and the other for setting up the jupyter notebook 
registration.

To make extra kernels known to the *sage* jupyter notebook server, you have 
to make sure that you run the configurations in the sage copy of python. 
You'd want to do something like "sage -sh" (on linux or OSX) to get a shell 
that is set up with the sage environment, and run the configurations in 
there.

An alternative is to register the sage kernel with the system-wide jupyter 
notebook. This has the advantage that other kernels that come prepackaged 
with your OS distribution are easier to install (just use your OS tools). 
It has the big disadvantage that currently, rhis is still not a 
straightforward process; it requires some hackery, so I would not recomment 
it as your first experiment.

A big disadvantage of the python ecosystem is that one often end up with 
multiple copies of python living side-by-side on your OS and it's often 
hard to tell which version gets run for what.

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