I would like to run a spark master from my Jupyter notebook. Currently I 
have Jupyter Hub running in Kubernetes successfully, and Kubespawner 
creates notebooks and it is awesome.

The problem is, in order to run spark master (driver) from my notebook to a 
Yarn cluster (not spark-submit, where the driver would be running in the 
cluster) the yarn executors cannot reach back to my notebook because the 
ports aren't exposed as a service.

Looking at Kubespawner code, I see there is a function it looks like to 
make a service with each pod, but I'm not sure how it would be invoked.

https://github.com/jupyterhub/kubespawner/blob/master/kubespawner/objects.py#L916:
def make_service(
    name,
    port,
    servername,
    owner_references,
    labels=None,
    annotations=None,
):
    """
    Make a k8s service specification for using dns to communicate with the 
notebook.
    Parameters
    ----------
    name:
        Name of the service. Must be unique within the namespace the object 
is
        going to be created in.
    env:
        Dictionary of environment variables.
    labels:
        Labels to add to the service.
    annotations:
        Annotations to add to the service.
    """

And then there's a call to make an ingress as well:

https://github.com/jupyterhub/kubespawner/blob/master/kubespawner/objects.py#L720

What are my missing? I can't see anything in the Kubespawner documentation 
about creating service ports (ideally using a LoadBalancer IP).

Side question: should I be using Enterprise Gateway? I'm slightly confused 
at what the mainstream way to deploy Jupyter notebooks in a multi-user 
environment should be.  I hadn't heard of the EG until I started 
researching this issue, and now I'm wondering if I'm deploying the right 
thing at all.

Thanks in Advance,

|> Greg

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