Welcome to Tutorials and Use Cases’s!#
This Gitlab repository collects and prepares Jupyter notebooks with coding examples on how to use state-of-the-art processing tools on big data collections. The Jupyter notebooks highlight the optimal usage of High-Performance Computing resources and adress data analysists and researchers which begin to work with resources of German Climate Computing Center DKRZ.
The Jupyter notebooks are meant to run in the Jupyterhub portal. See in this video the main features of the DKRZ Jupterhub/lab and how to use it. Clone this repositroy into your home directory at the DKRZ supercomputers Levante and Mistral. The contents will be visible from the Jupyterhub portal. When you open a notebook in the Jupyterhub, make sure you choose a recent Python 3 kernel on the Kernel tab (upper tool bar in the Jupyterhub). Such a kernel contains most of the common geoscience packages in current versions.
Direct and fast access to DKRZ’s data pools is a main benefit of the server-side data-near computing demonstrated here. Note that running the notebooks on your local computer will generally require much memory and processing resources.
Find a video tutorial here.
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