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YouTube video by NVIDIA Developer

Tutorial práctico de NVIDIA cuML: acelera scikit-learn sin cambiar código con cuml.accel, compara CPU vs GPU y construye pipelines nativos en GPU.

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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 PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN, while using synchronized timing to obtain meaningful performance measurements. We also build GPU-based manifold-learning and clustering pipelines with UMAP, t-SNE, HDBSCAN, and trustworthiness metrics; explore high-throughput forest inference with FIL; validate GPU-generated SHAP explanations; perform hyperparameter optimization with scikit-learn meta-estimators; and finally serialize trained models while examining portability between GPU and CPU environments. We configure the tutorial environment, define dataset sizes and benchmarking utilities, and verify that an NVIDIA GPU is available. We install and initialize RAPIDS cuML when necessary, set up CuPy and reproducibility controls, and create synchronized timing and result-tracking helpers. We also demonstrate cuml.accel by running an unmodified scikit-learn workload and comparing its CPU execution with GPU-accelerated execution. We work directly with the native...

cuda.ml 0.4.0 simplifies installation, expands tidymodels integration and tree-ensemble inference, and adds portable persistence for supported fitted models.

Episode 2 we'll start building the Sci-Fi inspired ML model to detect plants. Check out the last episode to see how we scoped our project. http://bit.ly/4nS76Wz Tech Stack: Anaconda 2025.06 + Python...

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An R interface to GPU-accelerated machine learning algorithms from RAPIDS cuML, powered by CUDA.

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