{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"libraft-cu12 provides CUDA-accelerated primitives and algorithms for machine learning and data mining, including dense and sparse linear algebra, solvers, statistics, and GPU infrastructure utilities.","skillfed_tags":["gpu-accelerated","cuda","machine-learning-primitives"],"use_cases":["Accelerate pairwise distance computations between large GPU-resident arrays for nearest-neighbor search.","Build distributed multi-GPU machine learning workflows using raft-dask infrastructure.","Implement custom GPU-accelerated algorithms by reusing RAFT's linear algebra and solver primitives.","Optimize sparse and dense matrix operations on GPU without writing custom CUDA kernels.","Integrate GPU-accelerated statistical sampling and model evaluation into existing GPU pipelines."],"what_it_does":"libraft-cu12 is a CUDA-accelerated C++ library providing low-level, reusable primitives for machine learning and data mining. It includes accelerated functions for dense and sparse linear algebra, matrix operations, solvers, statistical computations, and GPU infrastructure utilities. The library is designed as a building block for developers creating high-performance GPU applications, not for direct use by data scientists\u2014it centralizes core computations to reduce maintenance burden and enable future optimizations across dependent projects.\n\nThe package offers both C++ APIs (header-only with optional shared library for faster compilation) and Python bindings through pylibraft, which wraps host-accessible runtime APIs. Python users can pass objects supporting the __cuda_array_interface__ directly to RAFT functions. The library depends on cuda-toolkit, librmm-cu12, nvidia-nccl-cu12, nvidia-nvjitlink-cu12, and rapids-logger, making it suitable for environments with compatible GPUs.","worth_installing":"Yes, if you have a compatible NVIDIA GPU and are building GPU-accelerated machine learning or data mining applications. The library is actively maintained, permissively licensed under Apache-2.0, and provides well-tested primitives that reduce development time. Install only if you need low-level GPU acceleration for algorithms\u2014it is not intended for direct data science experimentation. Requires Python 3.11+, CUDA Toolkit, and medium install friction due to GPU runtime dependencies."},"id":"libraft-cu12","links":{"html":"https://skillfed.io/packages/libraft-cu12","md":"https://skillfed.io/packages/libraft-cu12.md","pypi":"https://pypi.org/project/libraft-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"libraft-cu12","python_support":"supports_current","summary":"RAFT: Reusable Algorithms Functions and other Tools (C++)"},"popularity":{"monthly_downloads":366894,"position":7201,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}