{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"GPU-accelerated machine learning algorithms for classification, regression, clustering, and dimensionality reduction, built on CUDA and integrated with scikit-learn-compatible APIs.","skillfed_tags":["gpu-accelerated","rapids-ecosystem","cuda"],"use_cases":["Train random forest or gradient boosting models on GPU for faster convergence on large datasets.","Accelerate hyperparameter tuning and cross-validation workflows by moving model training to GPU.","Build GPU-native ML pipelines in Dask for distributed training across multiple GPUs.","Replace CPU scikit-learn calls in existing code with GPU equivalents using the same API.","Perform fast clustering and dimensionality reduction on high-dimensional data."],"what_it_does":"cuML is a GPU-accelerated machine learning library from the RAPIDS ecosystem that brings standard ML algorithms to NVIDIA GPUs. It provides scikit-learn-compatible interfaces for algorithms including random forests, gradient boosting, clustering, and dimensionality reduction, allowing developers to scale training and inference on GPU hardware without rewriting model code.\n\nThe package wraps libcuml C++ implementations and depends on cuDF for GPU DataFrames, cuPy for GPU arrays, and RAFT for shared ML primitives. It targets developers who need to accelerate existing scikit-learn workflows on GPU clusters or single-GPU systems, and requires CUDA 12, Python 3.11+, and a compatible NVIDIA GPU. The library is actively maintained as part of the RAPIDS project and integrates with joblib, numba, and other ecosystem tools.","worth_installing":"Yes, if you have an NVIDIA GPU with CUDA 12 and need to accelerate scikit-learn-compatible workflows. The permissive Apache-2.0 license and active maintenance are favorable. No if you lack GPU hardware or need CPU-only ML. Medium friction from 18 dependencies and CUDA/GPU requirements; verify your GPU and CUDA setup before attempting installation."},"id":"cuml-cu12","links":{"html":"https://skillfed.io/packages/cuml-cu12","md":"https://skillfed.io/packages/cuml-cu12.md","pypi":"https://pypi.org/project/cuml-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"cuml-cu12","python_support":"supports_current","summary":"cuML - RAPIDS ML Algorithms"},"popularity":{"monthly_downloads":428122,"position":6746,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}