How to Reduce Python Runtime for Demanding Tasks | by Jiayan Yin | Nov, 2024

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, corresponding 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 special programming language in sure instances. In at this time’s article, I gained’t deal with algorithm-based strategies for bettering code effectivity. As an alternative, I’ll talk about sensible methods which might be each handy and simple to grasp.

As an instance, I’ll use the On-line Retail dataset, a publicly accessible dataset beneath a Artistic Commons Attribution 4.0 Worldwide (CC BY 4.0) license. You possibly can obtain the unique dataset Online Retail data from the UCI Machine Studying Repository. This dataset incorporates 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.