Hands-on workshops

Getting more Python Performance with Intel® optimized Distribution for Python
Jim Cownie

The slides used during the workshops can be downloaded from RSE18_Intel_Python_Workshop.

The K-Means example used is pretty close to the one available from the scikit-learn examples at http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/cluster/plot_color_quantization.html.

A complex version of the Black Scholes demo is available from Intel’s Python TCEs at https://github.com/triskadecaepyon/ep2017_tutorial_tune_performance

 

Using Singularity for High Performance Computing
Mihai Duta, Diamond Light Source
Andrew Gittings, University of Oxford

The materials for the workshop have been made available at https://github.com/mcduta/RSE-2018

 

Data vizualisation with Shiny
David Mawdsley, University of Manchester
Louise Lever, University of Manchester

The workshop materials are available at https://uomresearchit.github.io/RSE18-shiny-workshop/. A snapshot of the workshop is available at DOI 10.5281/zenodo.1409659

 

Make testing easy with pytest
Matt Williams, University of Bristol

The slides used during the workshops can be downloaded from pytest_rse18. The materials for the workshops have been made available at https://github.com/milliams/python_testing

 

A tried-and-tested workflow for software quality assurance
Mark Woodbridge, Research Computing Service, Imperial College London
Mayeul d’Avezac, Research Computing Service, Imperial College London

The materials for the workshop have been made available at https://doi.org/10.5281/zenodo.1409199