Posts by Sofiane Bendoukha

How to install R packages in different locations?

The default location for R packages is not writable and you can not install new packages. On demand we install new packages system-wide and for all users. However, it possible to install packages in different locations than root and here are the steps:

create a directory in $HOME e.g. ~/R/libs

How to install jupyter kernel for Matlab

In this tutorial, I will describe i) the steps to create a kernel for Matlab and ii) get the matlab_kernel working in Jupyterhub on Levante.

conda environment with python 3.9

../../_images/matlab.png

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conda env with ffmpeg and ipykernel

How to containerIze your jupyter kernel?

Containers are not supported on Levante at this point.

We have seen in this blog post how to encapsulate a jupyter notebook (server) in a singularity container . In this tutorial, I am going to describe how you can run a jupyter kernel in a container and make it available in the jupyter*.

Create a kernel from your own Julia installation

We already provide a kernel for Julia based on the module julia/1.7.0.

In order to use it, you only need to install ÌJulia:

Python environment locations

Kernels are based on python environments created with conda, virtualenv or other package manager. In some cases, the size of the environment can tremendously grow depending on the installed packages. The default location for python files is the $HOME directory. In this case, it will quickly fill your quota. In order to avoid this, we suggest that you create/store python files in other directories of the filesystem on Levante.

The following are two alternative locations where you can create your Python environment:

Dask jobqueue on Levante

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 (profiles).

Load the required clients

Dask jobqueue

Enable NCL Kernel in Jupyterhub

Can’t use NCL (Python) as kernel in Jupyter

This tutorial won’t work

Single jupyter notebooks in containers

you are using singularity containers

you need jupyter notebooks

FileNotFoundError: [Errno 2] No such file or directory

07 October 2020
01 August 2022
Sofiane Bendoukha
Jupyterhub
python jupyterhub kernels

See Wrapper packages here.

you:

New Singularity module deployed

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
Sofiane Bendoukha
containers

vs code is your favorite IDE

interested to use the remote extension

https://code.visualstudio.com/assets/docs/remote/remote-overview/architecture.png

Jupyterhub log file

Each Jupyter notebook is running as a SLUM job on Levante. 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.

Simple Dask clusters in Jupyterhub

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:

Dask Labextension

DKRZ Tech Talks

03 September 2020
Sofiane Bendoukha
news

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.