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This tutorial provides a practical guide to accelerating standard machine learning workflows using NVIDIA's cuML and RAPIDS frameworks on GPU hardware. It details performance optimizations for key algorithms like clustering and dimensionality reduction while addressing model explainability and deployment portability between CPU and GPU environments.
Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
This practical tutorial demonstrates how to build and accelerate machine learning workflows using NVIDIA cuML and RAPIDS. It covers GPU environment setup, zero-code scikit-learn acceleration with cuml.accel, performance benchmarking across key ML algorithms, manifold learning with UMAP and HDBSCAN, tree-model inference with FIL, and model explainability using GPU-accelerated SHAP The post Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference appeared first on MarkTechPost.
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…
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