{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Database","url":"https://skillfed.io/packages/category/database/3"}],"enrichment":{"capability":"Dask CUDA provides utilities for deploying and managing Dask workers on multi-GPU systems, enabling distributed computation across CUDA-enabled hardware.","skillfed_tags":["gpu-computing","distributed-systems","dask-ecosystem"],"use_cases":["Set up a multi-GPU Dask cluster on a single machine or cluster for distributed data processing with GPU acceleration.","Manage worker-to-GPU affinity automatically when scaling Dask workloads across available CUDA devices.","Integrate GPU-accelerated RAPIDS libraries (cuDF, cuML) with Dask's distributed scheduler for large-scale data science.","Monitor and coordinate GPU resource allocation across multiple Dask workers in a heterogeneous cluster.","Prototype and deploy distributed GPU workloads without manually writing worker spawning and GPU binding logic."],"what_it_does":"Dask CUDA is a utility library that handles the deployment and lifecycle management of Dask workers on systems with multiple CUDA-enabled GPUs. It provides a LocalCUDACluster abstraction that simplifies spinning up a distributed Dask cluster where each worker is bound to a specific GPU, allowing you to parallelize work across available hardware without manually managing worker-to-GPU assignment.\n\nThe library does not automatically convert existing Dask code to run on GPUs\u2014that remains the responsibility of your application code and the libraries you use (such as RAPIDS or CuPy). Instead, it focuses on the infrastructure layer: making it straightforward to create and manage a multi-GPU Dask cluster, handle worker placement, and coordinate resource allocation. It depends on click, numpy, pandas, zict, nvidia-ml-py for GPU monitoring, and rapids-dask-dependency for integration with the RAPIDS ecosystem.","worth_installing":"Yes, if you need to deploy Dask across multiple GPUs. The library is actively maintained, has no known vulnerabilities, and uses a permissive license. Install friction is moderate due to CUDA/driver dependencies, but those are prerequisites for any GPU work anyway. Not suitable if you lack GPU hardware or are looking for automatic GPU code conversion."},"id":"dask-cuda","links":{"html":"https://skillfed.io/packages/dask-cuda","md":"https://skillfed.io/packages/dask-cuda.md","pypi":"https://pypi.org/project/dask-cuda/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dask-cuda","python_support":"supports_current","summary":"Utilities for Dask and CUDA interactions"},"popularity":{"monthly_downloads":302340,"position":7824,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}