Posted in 2020
- 16 November 2020
According to the official Web site,
Dask jobqueue can be used to
deploy Dask on job queuing systems like PBS, Slurm, MOAB, SGE,
LSF, and HTCondor. Since the queuing system on Levante 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 you can launch
Dask cluster/workers as a Slurm jobs. In this case, Jupyterhub will play an interface role and the Dask
can use more than the allocated resources to your jupyterhub session
Load the required clients
- 05 November 2020
Can’t use NCL (Python) as kernel in Jupyter
This tutorial won’t work
- 07 October 2020
you are using singularity containers
you need jupyter notebooks
- 07 October 2020
- 01 August 2022
See Wrapper packages here.
- 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:
- 01 October 2020
vs code is your favorite IDE
interested to use the remote extension
- 25 September 2020
Each Jupyter notebook is running as a SLUM job on Levante. By default,
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
- 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:
- 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.