NVIDIA cuML Tutorial Demonstrates GPU-Accelerated Machine Learning Workflows with RAPIDS

According to the tutorial, the workflow begins by configuring the GPU environment and examining cuml.accel, a component that lets users accelerate existing scikit-learn workloads with minimal code changes. The article then moves to the native cuML API for direct CuPy and cuDF interoperability. This sequence covers both a low-friction path for existing scikit-learn code and a lower-level route for users who need GPU-native data structures and arrays.

The tutorial benchmarks CPU and GPU implementations of several algorithms: PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN. It uses synchronized timing to obtain meaningful performance measurements. In the provided code, a Timer class calls cp.cuda.runtime.deviceSynchronize before and after each measured block, then records the elapsed wall time. A record function stores CPU and GPU timings and prints a speedup ratio when both are available.

The workflow also builds GPU-based manifold-learning and clustering pipelines with UMAP, t-SNE, HDBSCAN, and trustworthiness metrics. It explores high-throughput forest inference with FIL, validates GPU-generated SHAP explanations, and performs hyperparameter optimization with scikit-learn meta-estimators. The tutorial concludes by serializing trained models and examining portability between GPU and CPU environments.

The article's setup code checks for an NVIDIA GPU through nvidia-smi and queries the GPU name, total memory, compute capability, and driver version. If cuML is not already available, the script installs cuml-cu12 from the NVIDIA package index. It attempts to pin the cuML version to the major and minor line of a preinstalled cuDF package; if cuDF is not found, it installs the latest stable cuml-cu12. The code notes that cuML requires scikit-learn 1.6 or later.

The tutorial uses a QUICK flag to scale the workload, with SEED set to 42 and SCALE set to 0.25 or 1.0. Data sizes include N_MAIN of 200,000 times SCALE with D_MAIN of 64, N_RF of 50,000 times SCALE with D_RF of 32, N_NN_INDEX of 50,000 times SCALE, N_NN_QUERY of 5,000 times SCALE, N_DBSCAN of 20,000 times SCALE, N_MANIFOLD of 60,000 times SCALE, and N_ACCEL of 80,000 times SCALE.

A separate accelerated script in the tutorial generates blobs with N_ACCEL samples, 32 features, 12 centers, and a fixed random state, then casts the data to float32. It times PCA with 8 components, K-Means with 12 clusters and one initialization, nearest-neighbor search with 8 neighbors on half the data and 5,000 query points, Ridge regression with alpha 1.0, and positive-constrained Ridge on 5,000 samples. The script prints the total model time under the label MODELTIME.

The tutorial is presented as a repeatable workflow rather than a single benchmark. Its sections are organized with helper functions for banners and error handling, so that a failing section is skipped and wall time is still reported. The article says cuML already available skips installation, while missing cuML triggers a pip install that takes roughly one to three minutes. If that install fails, the article points to an alternative Colab installation path using the RAPIDS AI CSP utilities repository.