cuML
A suite of GPU-accelerated machine learning algorithms with scikit-learn compatible APIs for 10-50x faster performance on large datasets.
What is cuML?
cuML is a GPU-accelerated machine learning library that provides scikit-learn compatible implementations of algorithms for clustering, regression, classification, dimensionality reduction, and more. It solves the problem of slow CPU-based ML training and inference on large datasets by leveraging NVIDIA GPUs for massive parallelism, delivering order-of-magnitude speed improvements.
Data scientists, ML researchers, and software engineers working with large tabular datasets who want to accelerate traditional ML workflows without rewriting code or learning low-level CUDA programming.
Developers choose cuML because it offers a frictionless transition from scikit-learn with matching APIs, provides 10-50x faster execution on GPUs, and scales to multi-node, multi-GPU clusters through Dask integration while maintaining full compatibility with the Python ML ecosystem.
Overview
cuML - RAPIDS Machine Learning Library
Use Cases
Best For
- Accelerating scikit-learn workflows on large datasets with GPU parallelism
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
