{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Provides multi-node multi-GPU distributed computing infrastructure for building GPU-accelerated algorithms with Dask, wrapping RAFT's host-accessible runtime APIs.","skillfed_tags":["gpu-accelerated","distributed-computing","cuda"],"use_cases":["Build distributed machine learning algorithms that scale across GPU clusters using RAFT's low-level primitives as building blocks.","Accelerate multi-node data processing workflows by offloading linear algebra, sparse operations, and statistical computations to GPUs.","Integrate GPU-accelerated RAFT functions into existing Dask applications without rewriting core algorithms.","Develop high-performance solvers and optimization routines that benefit from RAFT's centralized, reusable GPU kernels."],"what_it_does":"raft-dask-cu12 is the Dask integration layer for RAFT, enabling distributed GPU-accelerated computing across multiple nodes and GPUs. It wraps RAFT's host-accessible runtime APIs\u2014which don't require a CUDA compiler\u2014and layers them atop Dask's distributed task scheduler and NVIDIA's NCCL for inter-GPU communication. The package is part of the RAPIDS ecosystem and targets application developers and data source providers building high-performance distributed algorithms, not data scientists doing exploratory work.\n\nThe package depends on dask-cuda for GPU task scheduling, distributed-ucxx-cu12 for optimized communication, libraft-cu12 for the core accelerated primitives, and pylibraft-cu12 for Python bindings. It is actively maintained, supports Python 3.11 through 3.14, and carries no known security vulnerabilities. Installation requires a CUDA 12-compatible GPU environment and brings medium friction due to its compiled dependencies.","worth_installing":"Yes, if you are building distributed GPU-accelerated applications on multi-node clusters and want to leverage RAFT's low-level primitives through Dask. The package is actively maintained, carries no known vulnerabilities, and is permissively licensed. Install friction is moderate due to CUDA dependencies, but this is expected for GPU-targeting libraries. Not recommended for single-node work or exploratory data science\u2014use pylibraft-cu12 directly or RAPIDS cuML instead."},"id":"raft-dask-cu12","links":{"html":"https://skillfed.io/packages/raft-dask-cu12","md":"https://skillfed.io/packages/raft-dask-cu12.md","pypi":"https://pypi.org/project/raft-dask-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"raft-dask-cu12","python_support":"supports_current","summary":"Reusable Accelerated Functions & Tools Dask Infrastructure"},"popularity":{"monthly_downloads":186345,"position":9990,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}