Posts by Sofiane Bendoukha
SLURM update / Memory use
- 05 January 2021
Slurm config on Mistral has been updated to fix an issue related to memory use.
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.
Dask jobqueue on Mistral
- 16 November 2020
According to the official Web site, Dask jobqueue can be used to
deploy deploy Dask on job queuing systems like PBS, Slurm, MOAB, SGE,
LSF, and HTCondor. Since the queuing system on Mistral is Slurm, we are
going to show how to start a Dask cluster there. The idea is simple as
described here. The difference is that the workers can be distributed
through multiple nodes from the same partition. Using Dask jobqueue will
Dask cluster as a Slurm jobs.
In this case, Jupyterhub will often play an interface role and the Dask can use more than the allocated resources to your jupyterhub session (profiles).
Jupyter notebook/lab extensions
- 13 November 2020
Extensions bring additional interesting features to Jupyter*. Depending on the workflow in the notebook, users can install/enable extensions when required. Although is easy to add extensions to both Jupyter notebook an lab, the process can be sometimes annoying based on where jupyter is served from.
In general, installing and enabling extensions in your laptop or using
the start-jupyter script is straightforward, especially when the
developers well describe their extensions. There should be no
restrictions or permissions issues, just follow the instructions.
Enable NCL Kernel in Jupyterhub
- 05 November 2020
can’t use NCL (Python) as kernel in Jupyter
This tutorial won’t work
Single jupyter notebooks in containers
- 07 October 2020
you are using singularity containers
you need jupyter notebooks
FileNotFoundError: [Errno 2] No such file or directory
- 07 October 2020
you:
created your own conda env
Spawner options now savable
- 05 October 2020
We introduced a new feature to the preset and advanced options form.
This is a nice feature especially for the advanced options form, which
contain many fields. You can also reset the options to their initial
values by clicking on reset. The form options are saved in the
client’s browser every 10 seconds and are not lost if:
the browser crashes
New Singularity module deployed
- 02 October 2020
Recently, we deployed a new version of Singularity: 3.6.1. The old version is not available anymore due to many bugs reported by some users.
Errors like these are now fixed:
VS Code Remote on Mistral
- 01 October 2020
vs code is your favorite IDE
interested to use the remote extension
CF Python package added to the software tree
- 30 September 2020
According to this link:
The Python cf package is an Earth Science data analysis library that is built on a complete implementation of the CF data model. The cf package implements the CF data model 1 for its internal data structures and so is able to process any CF-compliant dataset. It is not strict about CF-compliance, however, so that partially conformant datasets may be ingested from existing datasets and written to new datasets. This is so that datasets that are partially conformant may nonetheless be modified in memory.
Jupyterhub log file
- 25 September 2020
Each Jupyter notebook is running as a SLUM job on MIstral. By default,
stdout and stderr of the SLURM batch job that is spawned by
Jupyterhub is written to your HOME directory on the HPC system. In
order to make it simple to locate the log file:
if you use the preset options form: the log file is named
jupyterhub_slurmspawner_preset_<id>.log.
GLIBC and the container-based workaround
- 23 September 2020
Have you ever tried to install/use a software on Mistral and seen a message like this?
This is for example one of the reasons why PyTorch is not available
in our python3 module. Those software packages require a newer version
of glibc. Unfortunately, most of Mistral nodes are based on CentOS 6
kernel. To check the version of glibc:
Simple Dask clusters in Jupyterhub
- 18 September 2020
There are multiple ways to create a dask cluster, the following is only an example. Please consult the official documentation. The Dask library is installed and can be found in any of the python3 kernels in jupyterhub. Of course, you can use your own python environment.
The simplest way to create a Dask cluster is to use the distributed module:
DKRZ Tech Talks
- 03 September 2020
It is our great pleasure to introduce the DKRZ Tech Talks. In this series of virtual talks we will present services of DKRZ and provide a forum for questions and answers. They will cover technical aspects of the use of our compute systems as well as procedures such as compute time applications and different teams relevant to DKRZ such as our machine learning specialists. The talks will be recorded and uploaded afterwards for further reference.
Go here for more information.