Accelerated Weka: GPU Machine Learning with Weka Workbench

Abstract

Recent advancements in Machine Learning (ML) have driven increased interest and demand in the field. However, the complexity and mathematical background required for many techniques can be challenging for newcomers and enthusiasts. For many years, Weka has been a cornerstone in introductory ML courses worldwide, offering a user-friendly interface that facilitates understanding of various methods. As an open-source software with an active community, Weka has continued to evolve with new attributes. With the growing size of datasets, users are increasingly looking for ways to accelerate computations. GPUs have become a preferred solution for efficiently building and deploying ML models. This paper presents Accelerated Weka, a software framework that integrates GPU-accelerated methods within Weka’s intuitive graphical user interface, significantly reducing execution times for large datasets while preserving Weka’s accessibility. Benchmark results indicate that Accelerated Weka can achieve 2,198x speedup on an A100 chip. The framework retains Weka’s GPL 3.0 license and offers a straightforward installation process through the Conda environment.