{"library":"dask-cuda","type":"library","category":null,"description":"Dask-CUDA is a Python library providing utilities to facilitate interactions between Dask and NVIDIA CUDA-enabled GPUs. It extends `dask.distributed`'s `LocalCluster` and `Worker` to manage and deploy Dask workers efficiently on GPU systems. Key features include automatic instantiation of per-GPU workers, setting CPU affinity for optimal performance, and robust GPU memory management, including spilling to host memory. It is a core component of the RAPIDS suite for GPU-accelerated data science. The library maintains an active development status with regular releases, currently at version 26.4.0.","language":"python","status":"active","version":"26.4.0","tags":["dask","cuda","gpu","distributed-computing","rapids","data-science","python"],"last_verified":"Mon May 25","install":[{"cmd":"pip install dask-cuda","imports":["from dask_cuda import LocalCUDACluster","from dask.distributed import Client"]},{"cmd":"pip install 'dask-cuda[cu12]' # for CUDA 12\npip install 'dask-cuda[cu13]' # for CUDA 13","imports":[]},{"cmd":"conda install -c rapidsai -c conda-forge dask-cuda cuda-version=13.1","imports":[]}],"homepage":"https://rapids.ai/dask-cuda","github":"https://github.com/rapidsai/dask-cuda","docs":"https://docs.rapids.ai/api/dask-cuda/stable/","changelog":null,"pypi":"https://pypi.org/project/dask-cuda/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":14.2,"avg_import_s":3.78,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/dask-cuda/compatibility"}}