{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Database","url":"https://skillfed.io/packages/category/database/4"}],"enrichment":{"capability":"Dask cuDF extends Dask DataFrame with a GPU-accelerated backend, enabling parallel and larger-than-memory data processing on NVIDIA GPUs using a pandas-like API.","skillfed_tags":["gpu-computing","distributed-dataframes","rapids"],"use_cases":["Process multi-gigabyte Parquet or CSV datasets on a single machine with multiple GPUs faster than CPU-based Dask.","Run groupby, join, and aggregation operations on GPU-resident data using familiar pandas-style syntax.","Prototype data pipelines that will later scale to multi-node GPU clusters without rewriting core logic.","Leverage GPU memory pools and spilling to host memory for workloads that exceed individual GPU VRAM.","Integrate GPU-accelerated dataframe operations into existing Dask workflows."],"what_it_does":"Dask cuDF is a GPU-accelerated extension for Dask DataFrame that brings RAPIDS cuDF's pandas-like API to distributed GPU computing. It automatically registers as the 'cudf' backend for Dask, allowing you to write familiar pandas-style code that executes on GPUs instead of CPUs. The package depends on cudf-cu12, cupy-cuda12x, fsspec, numpy, nvidia-ml-py, pandas, and rapids-dask-dependency to provide GPU computation and memory management. It handles coordination between Dask's task scheduler and GPU operations, making it possible to process datasets larger than a single GPU's memory by spilling to host memory.\n\nThe package is actively maintained, supports Python 3.11\u20133.14, and carries no known security vulnerabilities. Single-node multi-GPU workflows are the primary use case. The description notes that multi-node execution requires deploying a distributed cluster separately.","worth_installing":"Yes, if you have NVIDIA GPUs and need to process large datasets faster than CPU Dask. The low install friction, active maintenance, permissive Apache-2.0 license, and zero known vulnerabilities make it a solid choice. Requires CUDA 12 runtime and GPU hardware; not suitable for CPU-only environments."},"id":"dask-cudf-cu12","links":{"html":"https://skillfed.io/packages/dask-cudf-cu12","md":"https://skillfed.io/packages/dask-cudf-cu12.md","pypi":"https://pypi.org/project/dask-cudf-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dask-cudf-cu12","python_support":"supports_current","summary":"Utilities for Dask and cuDF interactions"},"popularity":{"monthly_downloads":186928,"position":9974,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}