@@ -10,7 +10,7 @@ The Jupyter notebooks are meant to run in the Jupyterhub server of the German Cl
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@@ -10,7 +10,7 @@ The Jupyter notebooks are meant to run in the Jupyterhub server of the German Cl
Please, choose the Python 3 unstable kernel on the Kernel tab (upper tool bar in the Jupyterhub). This kernel contains all the common geoscience packages.
Please, choose the Python 3 unstable kernel on the Kernel tab (upper tool bar in the Jupyterhub). This kernel contains all the common geoscience packages.
Besides the information of the Jupyterhub, in this [link](https://www.dkrz.de/up/systems/mistral/programming/jupyter-notebook) you can find how to run this Jupyter notebook in the DKRZ server out of the Jupyterhub, which will entail that you create the environment accounting for the required package dependencies. Running this Jupyter notebook in your premise, which is also known as [client-side](https://en.wikipedia.org/wiki/Client-side) computing, will also require that you install the necessary packages on you own but it will anyway fail because you will not have direct access to the data pool. Direct access to the data pool is one of the main benefits of the [server-side](https://en.wikipedia.org/wiki/Server-side) data-near computing we demonstrate in these tutorials and use cases.
Besides the information on the Jupyterhub, in this [link](https://www.dkrz.de/up/systems/mistral/programming/jupyter-notebook) you can find how to run Jupyter notebooks directly in the DKRZ server, that is, out of the Jupyterhub. It will entail that you create the environment accounting for the required package dependencies. Do not try to run the Jupyter notebooks in your premise, which is also known as [client-side](https://en.wikipedia.org/wiki/Client-side) computing. It will also require that you install the necessary packages on you own but it will anyway fail because you will not have direct access to the data pool. Direct access to the data pool is one of the main benefits of the [server-side](https://en.wikipedia.org/wiki/Server-side) data-near computing demonstrated in these tutorials and use cases :relaxed:.