Thanks for you progress and energy so far. I must confess that I am not that familiar with Jupyter, but looking at it, this appears to be a very attractive option to take up. For R we would miss out on Rstudio web - which is a big minus - but we can ignore that for now; though that should be revisited eventually for sure. Rstudio is a must have for many R users.
A clarifying question: Is the minimal Jupyter image basically a web-based command line? Would that suffice to do Stan development debugging things done?
The shared volumes as shown here:
seem to correspond to what we want. That is, users will want to modify their local files with these containers instead of having things disappear once the container live comes to an end.
As I understand, Jupyter is super flexible in terms of what kernel it runs, so that is great. I hope/assume that the notebook state is also somehow persistent?
Now, I recall that there was a question about pre-compiled images… of course, this is what we want! I would like to pull my favourite Stan flavour as a Jupyter image an start right away.
Wrt. to Windows: I know there is Docker for Windows running vanilla Windows in the container - but do we need that? I thought we can just have Docker run Linux on the Windows platform inside the container. I would not bother with Windows unless there is a good reason.
For the R package which you list in the pdf there can be potentially a minimal an a large “tidy verse” edition, I think (see Rocker).
The C++ libraries in Stan are those under Stan-math - so it’s CVODES, Intel TBB, some boost stuff is compiled, maybe some others, just nee to look these up. We should pre-build these libraries for the CmdStan based images.
Could we get a simple version of this up and running quickly - so that we can try it out without too much effort?