This event is a part of the "Best Practices for HPC Software Developers" webinar series, produced by the IDEAS Productivity Project. The HPC Best Practices webinars address issues faced by developers of computational science and engineering (CSE) software on high-performance computers (HPC) and occur approximately monthly.
|Webinar Title||Jupyter and HPC: Current State and Future Roadmap|
|Date and Time||2018-02-28 01:00 pm EST|
|Presenters||Matthias Bussonnier (UC Berkeley), Suhas Somnath (Oak Ridge National Laboratory), and Shreyas Cholia (National Energy Research Scientific Computing Center)|
|Registration, Information, and Archives||https://ideas-productivity.org/resources/series/hpc-best-practices-webinars/#webinar015|
Webinars are free and open to the public, but advance registration is required through the Event website. Archives (recording, slides, Q&A) will be posted at the same link soon after the event.
During the last few years the Jupyter notebook has become one of the tools of choice for the data science and high-performance computing (HPC) communities. This webinar will provide an overview of why Jupyter is gaining traction in education, data science, and HPC, with emphasis on how notebooks can be used as interactive documents for exploration and reporting. We will present an overview of how Jupyter works and how the network protocol can be leveraged for both a local single machine and remote-cluster work. We will discuss the nuts and bolts of how Jupyter has been deployed at NERSC as a case study in implementation of Jupyter in an HPC environment. This work implies learning the Jupyter ecosystem to take advantage of its powerful abstractions to develop custom infrastructure to satisfy policies and user needs. The webinar will show, as a use case, how Jupyter notebooks have transformed data discovery, visualization, and interactive analysis for the scanning probe and electron microscopy communities at Oak Ridge National Laboratory. It will also show how notebooks can seamlessly accommodate measurements from a wide variety of instruments through Pycroscopy, a framework for instrument agnostic data storage and analysis.
Matthias Bussonnier is a Postdoctoral Scholar at the University Of California Berkeley Institute of Datascience. Matthias is a Co-Founder of Jupyter and Core developer of IPython/Jupyter since 2012. Matthias is mostly working on the core Python component of Jupyter and the IPython kernel. He holds a PhD in Biophysics from the Institut Curie, Paris, France.
Suhas Somnath is a member of the OLCF’s Advanced Data and Workflows group and an expert in analytics for scanning probe microscopy (SPM). As a postdoctoral fellow at the Center for Nanophase Materials Science, he was among the five scientists who developed a fundamentally new approach towards capturing and analyzing data in SPM which was recognized with an R&D 100 award. He earned his PhD in mechanical engineering from the University of Illinois, Urbana-Champaign.
Shreyas Cholia is a computational systems engineer working on technologies to enhance the scientific big-data capabilities at NERSC. He is the lead developer of NEWT, a web service that allows you to access computing resources at NERSC. Shreyas also shares an appointment with the Data Science and Technology department in LBNL’s Computational Research Division. He graduated from Rice University, double majoring in Computer Science and Cognitive Sciences.