NVIDIA cuML and RAPIDS accelerate ML workflows on GPUs

This tutorial demonstrates how to implement machine learning workflows using NVIDIA's cuML and RAPIDS libraries for GPU acceleration. It covers setting up the GPU environment, accelerating scikit-learn workloads with cuML, and utilizing the native cuML API for direct interoperability with CuPy and cuDF. The guide includes benchmarking CPU and GPU performance for various algorithms like PCA, K-Means, logistic regression, and random forests, as well as building GPU-based pipelines for manifold learning and clustering with UMAP, t-SNE, and HDBSCAN. Additionally, it explores high-throughput inference, validates GPU-generated SHAP explanations, and discusses model serialization and portability between GPU and CPU environments. AI

IMPACT Accelerates machine learning tasks by leveraging GPU power for faster computation and model training.

RANK_REASON Tutorial on implementing ML workflows using existing libraries.

Read on MarkTechPost →

• CuPy
• DBSCAN
• hdbscan
• k-means clustering
• logistic regression model
• Nvidia
• principal component analysis
• random forest
• RAPIDS
• scikit-learn
• Shap
• t-Distributed Stochastic Neighbor Embedding
• Uniform Manifold Approximation and Projection

• infra
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →