{"library":"cuml-cu12","type":"library","category":null,"description":"cuML is a suite of GPU-accelerated machine learning algorithms provided by the RAPIDS ecosystem, designed to be API-compatible with scikit-learn for ease of use. It leverages NVIDIA CUDA for high-performance computing on GPUs, significantly speeding up tasks like clustering, regression, classification, and dimensionality reduction. It generally follows a monthly release cadence, aligning with the broader RAPIDS release schedule. The `cuml-cu12` package specifically targets CUDA 12.","language":"python","status":"active","version":"26.4.0","tags":["machine-learning","gpu","rapids","data-science","scikit-learn-compatible","cuda"],"last_verified":"Tue May 26","install":[{"cmd":"pip install cuml-cu12 cupy-cuda12x","imports":["from cuml.cluster import KMeans","from cuml.linear_model import LogisticRegression","from cuml.ensemble import RandomForestClassifier","from cuml.internals.validation import check_is_fitted"]},{"cmd":"conda install -c rapidsai -c conda-forge -c nvidia cuml=26.04 python=3.11 cudatoolkit=12.2","imports":[]}],"homepage":"https://rapids.ai","github":"https://github.com/rapidsai/cuml","docs":"https://docs.rapids.ai/api/cuml/stable/","changelog":null,"pypi":"https://pypi.org/project/cuml-cu12/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":20,"avg_install_s":94.7,"avg_import_s":10.08,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/cuml-cu12/compatibility"}}