|Speaker :||Quentin Lutz|
|Nokia Bell Labs France|
|Time:||10:30 am - 12:00 pm|
|Location:||Doctoral Training Center (EIT Digital)|
When in need of benchmarking an algorithm, running said algorithm on a given – likely wide – range of parameters is usually required. In order to speed up this process, parallel computing may be used. I will detail how Joblib enables such computations for CPU-only tasks with minimal overhead to the code.
Once those computations are over, visualisation follows. To this end, I will introduce Seaborn, a PyPlot-based alternative to Matplotlib featuring refreshed visuals and enhanced interoperability with the Pandas package.