Abstract
In recent years Jupyter notebooks have conquered class rooms and some scientists also enjoy their convenience to quickly evaluate ideas and check whether a more detailed study is justified. To lower the threshold for getting started with Jupyter notebooks and to ease sharing and collaborative use, offering a JupyterHub service is tempting. However, offering such a service for a larger science class also requires a compute backend with sufficient resources such that hundreds of notebooks can be run simultaneously. Since resource usage for teaching activities typically fluctuates significantly over the year, dedicated compute resources seem inefficient. In this paper we present an alternative by exploiting an existing high throughput computing cluster (BAF2) at the University of Bonn, which comes with the additional advantage that scientific users may use the very same software and data environment they also select for their batch jobs. To implement this, we used a novel approach which allowed us to integrate BAF2 execute nodes although they do not have inbound network connectivity. Therefore, it does not touch the security concept of the cluster. The very same technique can be used to integrate any compute resources without inbound network connectivity and thus allows one to overcome usual firewall restrictions. This design also simplifies exploiting remote resources e.g. offered by resource federations or cloud providers.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.