On Thu, Feb 16, 2017 at 6:23 AM, Thomas Kluyver <tak...@gmail.com> wrote:

> On 16 February 2017 at 10:00, DVD PS <d.perez-sua...@ucl.ac.uk> wrote:
>
>> we won't deploy it till we have some understanding of how many we can
>> handle
>
>
> I don't know of any tools to help you with this, unfortunately.
>
> Running the notebook servers takes ~40MB memory per user, and each Python
> kernel started is another ~30MB before loading any libraries. But resource
> use will often be dominated by what code users are running inside their
> notebooks.
>

We've been using JupyterHub since it was almost ready to use, and there
isn't an easy way to answer the question "how many students can a machine
handle?" We use a variety of kernels, and some languages are better than
others. We have some Javascript kernels that have a small impact on the
server, and Java kernels that just a dozen of students can bring a machine
to its knees (at least for a short time, while compiling). IPython is
pretty good, but of course it depends on what they are doing.

We use JupyterHub across Physics, Biology, and Computer Science. The
Physics courses tend to have the highest load per cell, partly due to their
style of processing, but also because of their problem sets.

Our server has 512 gigabytes of RAM, and 12 cores. It is actually just the
head node on a cluster. Our goal is to get it set up such that a student's
kernel would spin up on the node that is most available (probably using
docker). But we have to figure out how that plays with the other jobs and
the scheduler.

I had tried a CPU limiter in the past. Our load is getting so high now, we
may have to revisit that.

I'd be glad to help answer this question as we have a live, operating
setup. Feel free to contact me directly if there is something I can do
(e.g., provide other stats, run a test program, etc.)

-Doug


>
> Thomas
>
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