Hello Karsten,

one problem you will encounter when using remote kernels is that the kernel 
doesn't have access to the file system you're seeing in Jupyter Lab. That's 
almost sure to confuse users. They can edit files in their browser, but 
then the kernel doesn't see them. The notebook files will work fine, 
because they're interpreted by Jupyter and only code snippets are sent for 
execution to the kernel. But if there are data files, or libraries you'd 
like to import from the kernel, they cannot be read.

Another potential problem are libraries that come with a notebook 
extension, like ipywidgets with widgetsnbextension. One piece has to be 
installed in the remote kernel, the other in Jupyter. As long as you 
control both environments, you can keep versions in sync. When you control 
only the Jupyter side, you have to rely on someone else on the remote side 
to sync with what you're doing. 

So no, there isn't a standard way or best practice for using remote 
kernels. Afaict, the default UI for Jupyter Lab and Notebooks implicitly 
assumes that kernels are local, and that's true in most cases. Installation 
instructions for extension packages make the same assumption. You can get 
remote kernels to work, but the user experience will suffer.

Jupyter Kernel Gateway is one way to provide remote kernels. There's a demo 
called "nb2kg" that shows how to run a notebook server with all kernels 
being remote on one gateway. I'm not aware of a KG demo that would mix 
local and remote kernels, nor of an adaptation for Jupyter Lab.
https://github.com/jupyter/kernel_gateway_demos/tree/master/nb2kg

cheers,
  Roland

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