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
jupyterhubwithout it is shut down.
Control Panel:#
Either work with jupyterhub or jupyterlab
Navigate with a file browser
Launch
NotebooksorConsoles
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 andbashruns it instead of the python interpreterDefine a entire cell as
bashorlatexwith the%%magic
!ls
Analysis.rst License-and-Citation.rst
backups-archive.ipynb Preparation.rst
_build quality-assurance-cfchecker-ceda.ipynb
CMIP6-Data-Pool.ipynb quality-assurance-prepare.ipynb
_config.yml Quality-Assurance.rst
Curation.rst Replication.rst
Dashboards.rst Server-side-analysis.ipynb
Demos.rst Server-side-resources.rst
egest-service.ipynb services_overview.csv
FAQ.rst Services.rst
Find-and-Access.rst _static
globus-doc.ipynb statistics-cmip6.ipynb
index.rst _toc.yml
Ingestion.rst Tools-and-platforms.rst
intake-catalog-service.ipynb Transfer.rst
intake-view.rst Usage.rst
%%bash
ls
Analysis.rst
backups-archive.ipynb
_build
CMIP6-Data-Pool.ipynb
_config.yml
Curation.rst
Dashboards.rst
Demos.rst
egest-service.ipynb
FAQ.rst
Find-and-Access.rst
globus-doc.ipynb
index.rst
Ingestion.rst
intake-catalog-service.ipynb
intake-view.rst
License-and-Citation.rst
Preparation.rst
quality-assurance-cfchecker-ceda.ipynb
quality-assurance-prepare.ipynb
Quality-Assurance.rst
Replication.rst
Server-side-analysis.ipynb
Server-side-resources.rst
services_overview.csv
Services.rst
_static
statistics-cmip6.ipynb
_toc.yml
Tools-and-platforms.rst
Transfer.rst
Usage.rst
%%latex
\begin{align}
\frac{\partial u}{\partial t} + \nabla \cdot \left( \boldsymbol{v} u - D\nabla u \right) = f
\end{align}
Modules and extensions
pythonandjupyterbenefit from a quickly evolving package universeEmbed
htmlto 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