{"library":"libcuml-cu12","type":"library","category":null,"description":"RAPIDS cuML (CUDA-accelerated Machine Learning) is a suite of GPU-accelerated machine learning libraries and algorithms designed to be fully compatible with scikit-learn APIs, enabling users to transition seamlessly from CPU to GPU without significant code changes. It's part of the broader RAPIDS ecosystem for data science, optimized for CUDA 12. The current version is 26.4.0, following a monthly release cadence aligned with the RAPIDS project.","language":"python","status":"active","version":"26.4.0","tags":["GPU","Machine Learning","RAPIDS","CUDA","Scikit-learn API"],"last_verified":"Tue May 26","install":[{"cmd":"pip install libcuml-cu12","imports":["from cuml.cluster import KMeans","from cuml.ensemble import RandomForestClassifier","from cuml.linear_model import LinearRegression"]},{"cmd":"pip install cudf-cu12 # Recommended for data handling","imports":[]},{"cmd":"pip install dask distributed # Recommended for Dask integration","imports":[]}],"homepage":"https://rapids.ai","github":"https://github.com/rapidsai/cuml","docs":null,"changelog":null,"pypi":"https://pypi.org/project/libcuml-cu12/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":63,"avg_install_s":54,"avg_import_s":null,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/libcuml-cu12/compatibility"}}