Metadata-Version: 2.1
Name: dask-cuda
Version: 23.10.0
Summary: Utilities for Dask and CUDA interactions
Author: NVIDIA Corporation
License: Apache-2.0
Project-URL: Homepage, https://github.com/rapidsai/dask-cuda
Project-URL: Documentation, https://docs.rapids.ai/api/dask-cuda/stable/
Project-URL: Source, https://github.com/rapidsai/dask-cuda
Classifier: Intended Audience :: Developers
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: dask==2023.9.2
Requires-Dist: distributed==2023.9.2
Requires-Dist: pynvml<11.5,>=11.0.0
Requires-Dist: numpy>=1.21
Requires-Dist: numba>=0.57
Requires-Dist: pandas<1.6.0dev0,>=1.3
Requires-Dist: zict>=2.0.0
Provides-Extra: docs
Requires-Dist: numpydoc>=1.1.0; extra == "docs"
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx-click>=2.7.1; extra == "docs"
Requires-Dist: sphinx-rtd-theme>=0.5.1; extra == "docs"
Provides-Extra: test
Requires-Dist: pytest; extra == "test"

Dask CUDA
=========

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.

Example
-------

```python
from dask_cuda import LocalCUDACluster
from dask.distributed import Client

cluster = LocalCUDACluster()
client = Client(cluster)
```

Documentation is available [here](https://docs.rapids.ai/api/dask-cuda/nightly/).

What this is not
----------------

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](https://rapids.ai) and
[CuPy](https://cupy.chainer.org) with Dask is an ongoing effort.  You may wish
to read about this effort at [blog.dask.org](https://blog.dask.org) for more
information.  Additional information about Dask-CUDA can also be found in the
[docs](https://docs.rapids.ai/api/dask-cuda/nightly/).
