From Hours to Seconds: RAPIDS cuML and Scikit-learn Machine Learning Model Ensembling
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RAPIDS recently enhanced cuM's support with scikit-learn's ensemble model APIs to achieve more than 100x faster boosting, bagging, stacking, and more.
You can see the concept of Ensemble model as below figure. First of all, we need to train three different clssifier (KNN, NN, SVM), and you can get final prediction come from a vote between multiple independently trained models.
From the result shown as below, with just 50,000 records in the data, using cuML for the Logistic Regression and SVC estimators in the VotingClassifier provides a 100x speedup.
cuML's algorithms scale more effectively than their CPU equivalents because of the GPU's massive parallelism, high-bandwidth memory, and ability to process more data before saturating the available computational resources.
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