Have a look at Jupyter Enterprise Gateway, which enables and manage
remote kernels in multiple cluster environments including container
based ones such: Docker, Swarm, Kubernetes, etc

https://jupyter.org/enterprise_gateway/
https://github.com/jupyter/enterprise_gateway

Please let us know if you have specific questions after checking it out.

On Fri, Feb 1, 2019 at 10:59 AM Matt Morgis <[email protected]> wrote:
>
> For anyone in 2019 looking to do this, we built a prototype here: 
> https://github.com/tamera-lanham/ipython-kernel-docker
>
> Similar to the approach in the Gist, except instead of a Python file running 
> the container, we tell Jupyter to do it instead.
>
> On Tuesday, July 10, 2018 at 4:01:53 AM UTC-4, Ken Jiiii wrote:
>>
>> Hi guys,
>>
>> I think I have the same use case and I was wondering whether this discussion 
>> is still up to date.
>> The idea is to have a Jupyter running on a local machine which has one or 
>> more docker containers running at the same time. These containers provide 
>> for example different python versions like 3.6 and 3.7.
>> Now the question is how to add an external kernel to Jupyter. The kernel is 
>> of course running in the docker container.
>>
>> https://gist.github.com/mariusvniekerk/09062bc8974e5d1f4af6 is this approach 
>> still valid @ Marius?
>>
>> I have also read that it is possible to connect via ssh to a remote kernel 
>> in Jupyter but in that case SSH needs to be configured in the container.
>>
>> Can anybody tell me what solution is still working for him?
>>
>> Thanks a lot in advance and kind regards!
>>
>> Am Mittwoch, 26. Juli 2017 14:23:08 UTC+2 schrieb Ashwin Srinath:
>>>
>>> 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 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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>>>
>>>
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-- 
Luciano Resende
http://twitter.com/lresende1975
http://lresende.blogspot.com/

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