triton/apps/jupyter-gpu: Basic page to describe jupyter+GPUs (#757)

* triton/apps/jupyter-gpu: Basic page to describe jupyter+GPUs

* triton/apps/jupyter-gpu: minor update

* triton/apps/jupyter-gpu: misc updates

* triton/apps/jupyter-gpu: `--gpu` -> `--gpus` and format minor fix

---------

Co-authored-by: Sami Laine <sami.v.laine@aalto.fi>

Author: rkdarst

triton/apps/jupyter-gpu.rst

@@ -0,0 +1,83 @@
+Jupyter with GPUs
+=================
+
+.. warning::
+
+   Certain projects have funded hardware for Jupyter with GPUs.  The resources
+   are available to all Triton users, with priority given to the project
+   members.  Others can attempt to use in a preemptible queue (the jobs are
+   killed with no warning if a higher-priority user comes).
+
+   We are still tuning the parameters (run time, resources available,
+   etc.) to balance usefulness vs resource wastage.  There is no service
+   guarantee.  Let us know what is useful or not working.
+
+The normal OnDemand Jupyter does not include GPUs, because they are very
+expensive and Jupyter interactive work by its nature has lots of idling.
+However, some projects have ordered GPUs specifically for interactive work. The
+GPUs that have been dedicated for interactive Jupyter work are divided into
+separate virtual GPUs with less GPU-memory.
+
+
+.. highlight:: console
+
+
+Expected use case
+-----------------
+
+Remember, Jupyter+GPUs are designed to be used for testing and
+development, not production runs or real computation.  The GPU memory
+is limited, so you can test code but probably not even run
+moderately-sized models.  This is because any resources allocated to a
+Jupyter job are mostly idle.
+
+You should plan (from the beginning) how you will transition to batch
+jobs for your main computations.  For example, write and verify code
+in Jupyter with tiny data, then from the command line submit the code
+to run in the batch queue with much more resources::
+
+   $ sbatch --gpus=1 --wrap 'jupyter nbconvert --to notebook --execute mynotebook.ipynb --output mynotebook.$(date -Iseconds).ipynb'
+
+
+
+How it works
+------------
+
+* Use the normal OnDemand Jupyter app,
+  https://ondemand.triton.aalto.fi, as described in :doc:`jupyter`.
+* Select one of the interactive partitions (see below)
+* Your Jupyter session will start.  Note it has shorter timeouts that
+  other Jupyter sessions, to prevent inefficiency.  Once you have
+  resources, don't forget to use them.
+* When you are done with using the resources, remember to stop the session via
+  File > Shut Down.
+* There is no service guarantee, resources may be stopped or adjusted
+  anytime without warning.  Save often.
+
+.. list-table::
+   :header-rows: 1
+
+* * Name
+  * Who has access
+  * Resources
+
+* * Ellis H200 GPU
+  * ELLIS project staff (``ellis`` unix group). Still being formed, contact ASC
+    for access for now.
+  * 8 H200 GPUs split into a total of 56 vGPUs with 18G mem each.
+
+* * General H200 GPU
+  * Anyone, but sessions can be stopped without warning if a higher
+    priority user comes and needs the resources.
+  * Same as above
+
+Time limits and other parameters are visible in OnDemand (and not
+copied here since they may change).
+
+
+
+Contact
+-------
+
+Contact ASC/Science-IT us via the normal means, or the people in the
+table above for access to the resources.