Open AccessJupyterHub on an on-premises cloud -- a special focus on GPU Accelerated Machine Learning and 3D Visualization
Author(s) -
Markus Blank-Burian,
Jürgen Hölters,
Raimund Vogl
Publication year - 2021
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/f8vp
Subject(s) - cloud computing , computer science , virtual machine , cuda , visualization , operating system , server , focus (optics) , compiler , world wide web , artificial intelligence , physics , optics
At the IT department of the University of Mu ̈nster (WWU IT) we build a private IaaS cloud based on OpenStack and Kubernetes (WWU Cloud). This cloud provides a generic platform for data storage and service hosting. WWU IT operates a JupyterHub on WWU Cloud for use in research and education. Researchers have access to virtual GPUs from their Jupyter sessions. These may be used to compile and natively run CUDA accelerated code, e.g. for machine learning. Using VirtualGL, we also provide an accelerated X server in Jupyter sessions. X11 applications are then accessible from the browser using noVNC.