The tutorial provides a practical workflow for implementing machine learning workflows using NVIDIA cuML, RAPIDS, and GPU benchmarking tools. It begins with configuring a GPU environment and examining cuML’s accel functionality, which allows for accelerating existing scikit-learn workloads with minimal code changes. The implementation then benchmarks various algorithms, including PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN, across CPU and GPU implementations to measure speedups.
Synchronization techniques are employed to obtain meaningful performance measurements. The workflow also builds GPU-based manifold-learning and clustering pipelines utilizing UMAP, t-SNE, HDBSCAN, and trustworthiness metrics. Further exploration includes high-throughput forest inference with FIL, GPU-based SHAP explanations, and hyperparameter optimization using scikit-learn meta-estimators. The tutorial emphasizes the importance of maintaining data residency on the GPU through output-type controls, preventing unnecessary CPU-to-GPU copies that would negate performance gains.
The tutorial demonstrates serialization of trained models and portability between GPU and CPU environments. It showcases how to run an unmodified scikit-learn workload on GPU using cuML’s accel functionality, highlighting that no source code changes are required. The process involves installing and initializing RAPIDS cuML, setting up CuPy and reproducibility controls, and creating synchronized timing and result-tracking helpers.
Specifically, the tutorial details the use of cuML’s output-type controls to manage whether results remain on the GPU or return as NumPy arrays. It also includes a train-test split performed natively on the GPU, ensuring that the data remains resident on the device throughout the workflow. The tutorial explores zero-copy interoperability by inspecting device pointers to understand how CuPy, cuDF, and cuML components interact. Finally, it demonstrates the use of StandardScaler and train_test_split within a cuDF environment, emphasizing the importance of keeping output_type as CuPy/cuDF inside a pipeline.
