ToolsSeptember 13, 2026via MarkTechPost
Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
Why it matters
NVIDIA cuML and RAPIDS lower the barrier to GPU-accelerated ML for practitioners who know scikit-learn. This is a practical tools update for data teams evaluating whether to retool their ML stacks for speed.
Key signals
- NVIDIA cuML offers zero-code scikit-learn acceleration via cuml.accel
- Covers GPU benchmarking across key ML algorithms
- Includes manifold learning (UMAP, HDBSCAN), tree inference (FIL), and GPU-accelerated SHAP
- Published Sep 12, 2026
- Vendor tutorial/how-to (MarkTechPost)
- cuML and RAPIDS featured: zero-code scikit-learn acceleration via cuml.accel
- Manifold learning with UMAP and HDBSCAN covered
- Tree-model inference with FIL (Forest Inference Library)
- GPU-accelerated SHAP for model explainability
- Performance benchmarking across ML algorithms included
- Tutorial format: hands-on implementation, not research paper
The hook
GPU acceleration for ML workflows just got simpler: NVIDIA's cuML now runs scikit-learn code without rewrites.
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, …