Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.
The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.
- cuml.accel accelerates unmodified scikit-learn workloads with zero code changes
- Benchmarks PCA, K-Means, nearest neighbors, logistic regression, random forests, DBSCAN
- Includes UMAP, t-SNE, HDBSCAN clustering and SHAP explainability validation
- Datasets scale to 200,000 samples across synchronized timing benchmarks
- Covers FIL forest inference and GPU-to-CPU model portability
From the original article
10 min read at marktechpost.com
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.
By Sana Hassan · marktechpost.com
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