High performance computing with Python and RS-DAT

Making efficient use of high-performance systems can be hard. That is why the eScience Center and SURF have developed the Research Stack for a Distributed Analysis environmenT (RS-DAT). RS-DAT is a framework that makes it easier for non-experts to apply distributed data processing techniques. 

What will you learn in this training?

In this course, you will learn about how you can use the RS-DAT framework to scale up your data analyses on the supercomputing and storage systems at SURF, such as Snellius, Spider and dCache. 

With the help of user-friendly Jupyter notebooks, you will learn how to make use of Dask and Pydata tools, such as xarray. At the end of this training, you will know how to apply RS-DAT in your own research context.

For whom 

Researchers and research software engineers that would like to learn how to easily scale their data analyses to high-performance computing systems.

Requirements

• Personal computer and terminal with SSH.
• Experience with Python.
• Basic knowledge of Unix shell is useful. 

The training course is in English. 

Note: on May 27-29, we will organise a series of RS-DAT hackathon days, where you can get support on adopting RS-DAT for your research problems, and gain more advanced expertise in this area. Future trainings and hackathons will be listed here and on the TDCC website.