Jupyterhub
: A barrier-free entrance to DKRZ#
With an account for levante, you can connect via ssh
on a terminal interface to the system. This requires knowledge in shell programming.
As a user friendly alternative, we offer you the jupyterhub web service, which, by the way, also allows you to run terminals as with ssh
as well.
Features in combination with notebooks:
Software independent and interactive access to HPC resources
Tutorials prepared by experts and formatted as Jupyter-notebooks for a steep learning curve
Inline plot creation for a all in one solution
Fully supported and maintained at DKRZ
⇨ Advanced programming with a beginner friendly control
How it works:
What you have to do:
You log in via: https://jupyterhub.dkrz.de/hub/login
You request a notebook server
What DKRZ does in turn:
A server is spawned as a slurm job on levante. It uses resources as specified by your request.
Your server runs for as long as you specified in the request. In that time, you can close the tab and revisit
jupyterhub
without it is shut down.
Control Panel:#
Either work with jupyterhub or jupyterlab
Navigate with a file browser
Launch
Notebooks
orConsoles
Why do we promote Jupyter-Notebooks
?#
Cells
Clear view: Mark Cells as Code, Markdown or Raw
Interactive: Run a cell individually without running the entire script
Code language: Python, but also Bash, R, Julia, Latex:
Use
!
before the commands andbash
runs it instead of the python interpreterDefine a entire cell as
bash
orlatex
with the%%
magic
!ls
Analysis.rst Usage.rst
CMIP6-Data-Pool.ipynb _build
Curation.rst _config.yml
Dashboards.rst _static
Demos.rst _toc.yml
FAQ.rst backups-archive.ipynb
Find-and-Access.rst egest-service.ipynb
Ingestion.rst globus-doc.ipynb
License-and-Citation.rst index.rst
Preparation.rst intake-catalog-service.ipynb
Quality-Assurance.rst intake-esgf.ipynb
Replication.rst intake-view.rst
Server-side-analysis.ipynb quality-assurance-cfchecker-ceda.ipynb
Server-side-resources.rst quality-assurance-prepare.ipynb
Services.rst services_overview.csv
Tools-and-platforms.rst statistics-cmip6.ipynb
Transfer.rst wdcc_dataset_tabulator.ipynb
%%bash
ls
Analysis.rst
CMIP6-Data-Pool.ipynb
Curation.rst
Dashboards.rst
Demos.rst
FAQ.rst
Find-and-Access.rst
Ingestion.rst
License-and-Citation.rst
Preparation.rst
Quality-Assurance.rst
Replication.rst
Server
-side-analysis.ipynb
Server-side-resources.rst
Services.rst
Tools-and-platforms.rst
Transfer.rst
Usa
ge.rst
_build
_config.yml
_static
_toc.yml
backups-archive.ipynb
egest-service.ipynb
globus-doc.ipyn
b
index.rst
intake-catalog-service.ipynb
intake-esgf.ipynb
intake-view.rst
quality-assurance-cfcheck
er-ceda.ipynb
quality-assurance-prepare.ipynb
services_overview.csv
statistics-cmip6.ipynb
wdcc_data
set_tabulator.ipynb
%%latex
\begin{align}
\frac{\partial u}{\partial t} + \nabla \cdot \left( \boldsymbol{v} u - D\nabla u \right) = f
\end{align}
Modules and extensions
python
andjupyter
benefit from a quickly evolving package universeEmbed
html
to the notebookAutomatic code formatting when executing cell
from IPython.display import HTML, display, Markdown, IFrame
display(Markdown("Time series of three different data pool disk space measures. DKRZ has published about 1.5 PB, 2.5 PB are replicated data from other data nodes. An average CMIP6 dataset contains about 5 files and covers 4GB."))
IFrame(src="https://swift.dkrz.de/v1/dkrz_a44962e3ba914c309a7421573a6949a6/Pool-Statistics/pool-timeseries-hvplot.html",width="900",height="550",frameborder="0")
Time series of three different data pool disk space measures. DKRZ has published about 1.5 PB, 2.5 PB are replicated data from other data nodes. An average CMIP6 dataset contains about 5 files and covers 4GB.
Kernels:
Predefined Software Environments can be set in the upper right by using a specific kernel
On Jupyterhub, you can choose between a hands full of different configurations
Customized kernels can be implemented with conda. See the docs for a tutorial