We have used Singularity (http://singularity.lbl.gov/) containers in
Jupyter Notebooks with relative ease. Some notes available here:

https://github.com/clemsonciti/singularity-in-jupyter-notebook

Thanks,
Ashwin

On Tue, Jul 25, 2017 at 5:10 AM, Stojan Jovanović <
[email protected]> wrote:

> Hi James,
>
> I'm currently buidling something very similar to what you're talking about.
>
> I've currently got it set up so that I can access multiple Dockers,
> containing isolated machine learning models, through a Jupyter notebook
> (located in a third Docker), via SSH.
>
> It wasn't super difficult to do, although I'm not claiming it was done
> very elegantly.
>
> If you're interested, you can take a look here https://github.com/
> stojan211287/DockerSSH. I've uploaded a minimal example, consisting of
> one "drone" and one "overlord" container. The overlord issues commands via
> SSH, the drone complies and delivers.
>
> As it stands now, I've based the images on Alpine 3.6 and am currently
> using them as base images for further development.- the overlord get
> Jupyter installed on top of it, and the drone, for example, can host
> scikit-learn.
>
> On Wednesday, 24 May 2017 23:10:34 UTC+2, James wrote:
>>
>> Hey, sorry to revive this thread again, but having docker container
>> kernels (and not whole jupyter server systems) would be very useful for
>> me.  My use case is having certain hard to build scientific software
>> installed within the container.  That way you could call out to them using
>> python's subprocess calls from within the notebook.  My goal would be to
>> make several kernels, accessible from the same notebook server, to act as a
>> toolkit of sorts for my lab.  Ideally having the kernels in containers
>> would make them easy to share and install in sister labs at other
>> institutions for use in their Jupyter ecosystem.  Thank you for any
>> guidance!
>>
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