This presentation introduces cuML, NVIDIA's GPU-accelerated machine learning library, designed to address the challenges of ever-growing data sizes and the time-consuming iterative nature of the full ML workflow. As a core component of the open-source RAPIDS ecosystem, cuML provides Scikit-learn-like Python APIs built upon optimized CUDA C++ algorithms and fundamental GPU primitives. The architecture leverages Dask for seamless scaling across multi-GPU and multi-node environments, enabling both data distribution and model parallelism. Benchmarks demonstrate drastic performance improvements, showcasing speedups from hours to minutes or seconds for common algorithms like Linear Regression, tSVD, Nearest Neighbors, and end-to-end XGBoost pipelines, significantly accelerating data preparation, training, and tuning. The roadmap indicates continuous expansion of algorithms and features to further enhance GPU-accelerated data science.
Summarizing cuML: GPU Accelerated Machine Learning for Extreme Data
0:00 Introduction to cuML and the Data Problem: Introduces Onur Yilmaz and Corey Nolet from NVIDIA's RAPIDS cuML team. Highlights the challenge of ever-growing data sizes in machine learning, which necessitates extensive exploration, iteration, cross-validation, and grid search, leading to prolonged processing times (hours/days) if not fully accelerated.
0:00 The Need for End-to-End Acceleration: Argues that accelerating only model training is insufficient; the entire ML workflow, from ETL and feature engineering to training and tuning, requires end-to-end GPU acceleration to overcome the time barrier and enable faster iteration.
0:00 RAPIDS Architecture for GPU Data Science: Presents cuML as a core component of the open-source RAPIDS ecosystem, designed for GPU-accelerated data science. It mimics popular Python libraries like Pandas (cuDF) and Scikit-learn (cuML) and leverages Dask for multi-GPU and multi-node distributed computing.
0:00 cuML API and Primitives: Explains cuML's layered API structure, offering a Scikit-learn-like Python interface for data scientists and a CUDA C++ API for developers. It emphasizes a library of GPU-accelerated linear algebra and statistical primitives as foundational building blocks for ML algorithms.
0:00 Supported Algorithms: Details a wide range of GPU-accelerated algorithms available in cuML, including classification/regression (Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors), clustering (K-Means, DBSCAN, Spectral Clustering), decomposition & dimensionality reduction (Principal Components, Singular Value Decomposition, UMAP, Spectral Embedding), and statistical inference (Kalman Filtering, Bayesian Inference, Gaussian Mixture Models, Hidden Markov Models), along with timeseries forecasting and recommendation algorithms.
0:00 Deep Dive: Linear Regression Example: Illustrates the internal workings of cuML using Linear Regression (OLS) as an example, demonstrating how high-level Python calls are translated down to optimized CUDA C++ algorithms and low-level GPU primitives like QR Decomposition for maximum performance.
0:00 Benchmarking Superior Performance: Showcases significant performance gains, with cuML achieving substantial speedups compared to CPU-based solutions. Examples include reducing Linear Regression from 40 minutes to 1 minute (225GB), tSVD from 1.6 hours to 1.5 minutes (220GB), and Nearest Neighbors from 4+ hours to 30 seconds (128GB).
0:00 End-to-End XGBoost Acceleration: Demonstrates the power of cuDF + XGBoost for full end-to-end pipelines, showing DGX-2 and multi-DGX-1 GPU clusters outperforming large CPU clusters by factors of 10-15x on a 200GB CSV dataset, validating the need for fully in-GPU processing.
0:00 cuML Roadmap and Future Development: Outlines the ongoing development, including new algorithms like Stochastic Gradient Descent, UMAP, and multi-GPU Linear Regression/Truncated SVD in cuML 0.6, along with improvements in hyperparameter tuning and reduced memory requirements. Future plans include K-Means, DBSCAN, and PCA for Dask-cuML in versions 0.7/0.8, with the goal of continuing to accelerate the entire ML pipeline.