Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

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.

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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 cuML API and explore how GPU-resident data moves between CuPy, cuDF, and cuML components. We inspect device pointers to understand zero-copy interoperability and use cuML output-type controls to manage whether results remain on the GPU or return as NumPy arrays. We also perform a GPU-native train-test split so that our data remains on the device throughout the workflow.

We benchmark scikit-learn and cuML implementations of PCA, K-Means, nearest neighbors, logistic regression, random forests, and DBSCAN. We generate datasets on the GPU, synchronize CUDA operations for fair timing, and record the speedup each accelerated algorithm achieves. We also compare model behavior and retain the trained cuML random forest so that we can reuse it later in the tutorial.

We build an unsupervised GPU pipeline using UMAP and t-SNE to reduce high-dimensional data into two-dimensional embeddings. We evaluate UMAP configurations with the trustworthiness metric, select the strongest embedding, and apply HDBSCAN to identify clusters and noise points. We then visualize the resulting embeddings and compare their structures using the known ground-truth cluster labels.

We focus on accelerating inference for tree-based models after training. We train a scikit-learn random forest on the CPU, load it into the cuML Forest Inference Library when supported, and optimize the inference configuration for the current GPU batch size. We compare CPU and GPU prediction times and validate that the predicted probabilities remain numerically consistent.

We use cuMLs GPU-based permutation explainer to calculate SHAP values for a Ridge regression model and validate those explanations against the analytical linear solution. We test SHAP additivity and visualize feature importance to confirm that the computed attributions behave as expected. We also combine cuML estimators with scikit-learns RandomizedSearchCV to perform cross-validated hyperparameter optimization while fitting the model on the GPU.

We serialize a trained cuML random forest with pickle, restore it, and verify that its predictions remain unchanged after the round trip. We aggregate the CPU and GPU timing results collected throughout the tutorial and visualize the resulting speedups on a logarithmic chart. Finally, we run every tutorial section in sequence, print the accumulated practical takeaways, and report the total runtime of the complete workflow.

In conclusion, we implemented a comprehensive understanding of how NVIDIA cuML integrates GPU acceleration into both existing scikit-learn workflows and fully GPU-native machine learning pipelines. We compared computational performance across several core algorithms, managed device-resident data efficiently with CuPy and cuDF, evaluated unsupervised representations and clustering quality, accelerated tree-model inference, and generated interpretable SHAP explanations directly on the GPU. We also showed that familiar scikit-learn utilities such as RandomizedSearchCV can work alongside cuML estimators, preserving established machine learning development patterns while benefiting from GPU execution.

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