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cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.
Features
- Module Configuration
- Preprocessing, Metrics, and Utilities
- Dimensionality Reduction and Manifold Learning
- Multi-Node, Multi-GPU Algorithms
- Time Series
- Model Explainability
Project Samples
Categories
Machine LearningLicense
Apache License V2.0Follow cuML
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