Sensible methods to speed up heavy workloads with GPU optimization in Python
One of many largest challenges that knowledge scientists face is the prolonged runtime of Python code when dealing with extraordinarily massive datasets or extremely complicated machine studying/deep studying fashions. Many strategies have confirmed efficient for enhancing code effectivity, equivalent to dimensionality discount, mannequin optimization, and have choice — these are algorithm-based options. Another choice to handle this problem is to make use of a unique programming language in sure circumstances. In right this moment’s article, I gained’t concentrate on algorithm-based strategies for enhancing code effectivity. As a substitute, I’ll focus on sensible methods which can be each handy and simple to grasp.
For instance, I’ll use the On-line Retail dataset, a publicly out there dataset underneath a Artistic Commons Attribution 4.0 Worldwide (CC BY 4.0) license. You possibly can obtain the unique dataset On-line Retail knowledge from the UCI Machine Studying Repository. This dataset comprises all of the transactional knowledge occurring between a particular interval for a UK-based and registered non-store on-line retail. The goal is to coach a mannequin to foretell whether or not the shopper would make a repurchase and the next python code is used to attain the target.
