Sensible methods to speed up heavy workloads with GPU optimization in Python
One of many largest challenges that information 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 bettering code effectivity, equivalent to dimensionality discount, mannequin optimization, and have choice — these are algorithm-based options. An alternative choice to deal with this problem is to make use of a special programming language in sure circumstances. In at present’s article, I received’t deal with algorithm-based strategies for bettering code effectivity. As a substitute, I’ll talk about sensible methods which are each handy and straightforward to grasp.
As an instance, I’ll use the On-line Retail dataset, a publicly out there dataset beneath a Artistic Commons Attribution 4.0 Worldwide (CC BY 4.0) license. You possibly can obtain the unique dataset On-line Retail information from the UCI Machine Studying Repository. This dataset accommodates all of the transactional information occurring between a selected 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.
