This online workshop is meant to give an overview of working with research data in Python using general libraries for storing, processing, analysing and sharing data. The focus is on improving performance. After covering tools for performant processing (netcdf, numpy, pandas, scipy) on single workstations the focus shifts to parallel, distributed and GPU computing (snakemake, numba, dask, multiprocessing, mpi4py).
The workshop will be streamed via Twitch so that anyone may follow along without registration. However, ENCCS is hosting a Zoom room with expert helpers who will organise breakout rooms and provide hands-on assistance during exercise sessions.
ENCCS and SciLifeLab are organising a webinar giving an intro on GPU programming in Python and Julia. This event is part of the Tools for AI/ML research in life sciences. Practical information Title: Practical intro to GPU programming in Python & JuliaSpeaker: Yonglei Wang, PhD, Training Coordinator and Research Software Engineer and HPC application expert at ENCCSAbstract: Availability of […]
Join us at the Stockholm Python Group meetup to learn more about Python performance, profiling, and parallelising.
Registrations are closed for this event. About the webinar In the past decade, Graphics Processing Units (GPUs) have ignited the dynamic evolution of data science. But GPUs can do a lot more than machine learning - these powerful devices can accelerate and massively parallelise any general-purpose computational load in domains involving big data and heavy […]
Registrations are closed for this event. Python for scientific computing is a medium-advanced course in Python tools such as NumPy, SciPy, Matplotlib, and Pandas. It is suitable for people who know basic Python and want to know some internals and important libraries for science - basically, how a typical scientist actually uses Python. Python is […]
Registrations are closed for this event. About the webinar In this webinar, we focus on GPU-accelerated computing with Julia, one of the most popular high-level, general-purpose dynamic programming languages for science, engineering, data analytics, and deep learning applications. Starting from the basic syntax of Julia, we will span topics on multiple-dispatch paradigm, metaprogramming, and then […]
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951732. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and its associated countries .
