Working Environment
After logging in to JupyterHub.nrw, you can select the required resources (e.g., CPU and RAM) as well as a suitable working environment on the Spawn page. After logging in and being redirected to the JupyterHub.nrw website, you can select the resources required for your work (e.g., CPU and RAM).
Resource Quotas¶
Resource quotas depend on the user’s status and user group. Higher resource quotas of up to 16 virtual CPUs (vCPUs), 64 GB of RAM, and additional graphics processing units (GPUs) can be requested through our support-Team. Alternatively, High Performance Computing (HPC) can be used, if available. Members of the University of Münster can use the PALMA cluster for this purpose. Further information can be found in the PALMA documentation.
The resource quotas depend on the respective user's status and user groups. Higher resource quotas up to 16 vCPUs and 64GB of RAM and additional GPUs can be requested.
Supported Environments (Notebook Images)¶
Various configurations (notebook images) are available for different purposes. Depending on the image you choose, different programs and packages are preinstalled. The following notebook images are (currently) available for every university:
- Data Science
- Data Science + Machine Learning
- Software Development
- Rescue Mode
Starting the Server¶
Now you can start the server. It may take some time for your working environment (JupyterLab) to be ready.
Once you have selected the resources and the image, you can start the server. It may take a while for the JupyterLab environment to be ready.
Changing the Working Environment¶
If you want to start a different image, you must first stop the server and then restart it. To do this, click File > Hub Control Panel > Stop My Server in JupyterLab. Then click Start My Server.
Further Information¶
For information on installing specific packages, see the Custom Environments section.
Useful links for using JupyterLab and Jupyter Notebooks