Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
In this tutorial, we implement NVIDIA cuML as a GPU-accelerated machine learning framework and build a practical workflow that demonstrates how RAPIDS can accelerate familiar data science and machine learning tasks. We begin by configuring the GPU environment and examining cuml.accel, which lets us accelerate existing scikit-learn workloads with minimal code changes, before moving to the native cuML API for direct CuPy and cuDF interoperability. We then benchmark CPU and GPU implementations of
This tutorial demonstrates how to build machine learning workflows using NVIDIA cuML, a GPU-accelerated framework that can speed up familiar data science tasks. The implementation covers benchmarking CPU versus GPU performance across multiple algorithms including PCA, K-Means, logistic regression, random forests, and clustering methods, while also exploring model explainability with SHAP and hyperparameter optimization. The tutorial shows that existing scikit-learn code can be accelerated with minimal changes through cuml.accel, and includes examples of serializing and moving trained models between GPU and CPU environments.
Explainer: How to Actually Read an AI Benchmark (Without Being Fooled)
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