Traditional machine learning models like Random Forest are great for performing analysis of data. However, with the ever-growing data, the time required to create an end to end pipeline is very high. RAPIDS is an open source suite of GPU-accelerated libraries with the aim of accelerating end-to-end data science pipelines. cuML is a machine learning library in the RAPIDS ecosystem focused on accelerating traditional machine learning algorithms.

This talk will open with an explanation of the current situation, the need for speed-up, and an overview of the cuML library. Then the talk will take a deep dive into the implementation of two important algorithms, Decision Trees and Random Forest. We will also cover a new library, Forest Inference Library (FIL) which performs GPU accelerated inference of pre-trained XGBoost, LightGBM, Scikit-learn Random Forest and cuML’s Random Forest models. In addition, the talk will also walk through an end-to-end workflow showcasing how the cuML API design adheres to the Scikit-learn conventions, the tradeoffs of using the library, and the conditions which will provide better performance. The aim of this talk is to show data scientists and researchers the need for, and the ease with which, they can accelerate their code.