rapidsai/cuml
GPU-accelerated machine learning library providing scikit-learn-compatible implementations of ML algorithms.
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cuML is a RAPIDS library that implements machine learning algorithms and mathematical primitives on GPUs, enabling data scientists to run traditional tabular ML tasks without CUDA programming expertise. It provides Python APIs compatible with scikit-learn and supports multi-GPU operations via Dask. GPU implementations offer 10-50x speedups over CPU equivalents for algorithms like DBSCAN and NearestNeighbors.
Frequently asked
- What is rapidsai/cuml?
- GPU-accelerated machine learning library providing scikit-learn-compatible implementations of ML algorithms.
- Is cuml open source?
- Yes — rapidsai/cuml is open source, released under the Apache-2.0 license.
- What language is cuml written in?
- rapidsai/cuml is primarily written in Python.
- How popular is cuml?
- rapidsai/cuml has 5.2k stars on GitHub.
- Where can I find cuml?
- rapidsai/cuml is on GitHub at https://github.com/rapidsai/cuml.