SLURM update / Memory use#
Slurm jobs (Jupyterhub sessions) that exceed the allocated memory will be killed by Slurm. Jupyterhub session needs to be restarted.
Prior the update, some Slurm jobs continue consuming the available
memory (and even swap) of the allocated node and exceed the allocated
memory (set in sbatch
or srun
). If this occurs, it also affect
other jobs/users.
Now?#
How can I recognize that my session is stopped#
you will probably see a error message like this:
How to restart?#
click again on Home
and then on Launch Server XXXX
Solution(s)#
If the issue is related to memory, the obvious solution is to restart
the Jupyterhub session with a higher memory. Either selecting a
different profile if you are using preset
or setting up the memry
with --cpus-per-task
/--mem
.