Various utilities to improve deployment and management of Dask workers on CUDA-enabled systems.
This library is experimental, and its API is subject to change at any time without notice.
from dask_cuda import LocalCUDACluster
from dask.distributed import Client
cluster = LocalCUDACluster()
client = Client(cluster)
Documentation is available here.
This library does not automatically convert your Dask code to run on GPUs.
It only helps with deployment and management of Dask workers in multi-GPU systems. Parallelizing GPU libraries like RAPIDS and CuPy with Dask is an ongoing effort. You may wish to read about this effort at blog.dask.org for more information. Additional information about Dask-CUDA can also be found in the docs.
FAQs
Utilities for Dask and CUDA interactions
We found that dask-cuda demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
Security News
Django has updated its security policies to reject AI-generated vulnerability reports that include fabricated or unverifiable content.
Security News
ECMAScript 2025 introduces Iterator Helpers, Set methods, JSON modules, and more in its latest spec update approved by Ecma in June 2025.