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        "author": "Lev E. Givon",
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        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
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            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "Package Description\n-------------------\nscikit-cuda provides Python interfaces to many of the functions in the CUDA\ndevice/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of\nNVIDIA's `CUDA Programming Toolkit <http://www.nvidia.com/cuda/>`_, as well as\ninterfaces to select functions in the `CULA Dense Toolkit <http://www.culatools.com/dense>`_.\nBoth low-level wrapper functions similar to their C counterparts and high-level\nfunctions comparable to those in `NumPy and Scipy <http://www.scipy.org>`_ are provided.\n\n.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.3229433.svg\n    :target: http://dx.doi.org/10.5281/zenodo.3229433\n    :alt: 0.5.3\n.. image:: https://img.shields.io/pypi/v/scikit-cuda.svg\n    :target: https://pypi.python.org/pypi/scikit-cuda\n    :alt: Latest Version\n.. image:: https://img.shields.io/pypi/dm/scikit-cuda.svg\n    :target: https://pypi.python.org/pypi/scikit-cuda\n    :alt: Downloads\n.. image:: http://prime4commit.com/projects/102.svg\n    :target: http://prime4commit.com/projects/102\n    :alt: Support the project\n.. image:: https://www.openhub.net/p/scikit-cuda/widgets/project_thin_badge?format=gif\n    :target: https://www.openhub.net/p/scikit-cuda?ref=Thin+badge\n    :alt: Open Hub\n\nDocumentation\n-------------\nPackage documentation is available at\n`<http://scikit-cuda.readthedocs.org/>`_.  Many of the high-level\nfunctions have examples in their docstrings. More illustrations of how\nto use both the wrappers and high-level functions can be found in the\n``demos/`` and ``tests/`` subdirectories.\n\nDevelopment\n-----------\nThe latest source code can be obtained from\n`<https://github.com/lebedov/scikit-cuda>`_.\n\nWhen submitting bug reports or questions via the `issue tracker \n<https://github.com/lebedov/scikit-cuda/issues>`_, please include the following \ninformation:\n\n- Python version.\n- OS platform.\n- CUDA and PyCUDA version.\n- Version or git revision of scikit-cuda.\n\nCiting\n------\nIf you use scikit-cuda in a scholarly publication, please cite it as follows: ::\n\n    @misc{givon_scikit-cuda_2019,\n              author = {Lev E. Givon and\n                        Thomas Unterthiner and\n                        N. Benjamin Erichson and\n                        David Wei Chiang and\n                        Eric Larson and\n                        Luke Pfister and\n                        Sander Dieleman and\n                        Gregory R. Lee and\n                        Stefan van der Walt and\n                        Bryant Menn and\n                        Teodor Mihai Moldovan and\n                        Fr\\'{e}d\\'{e}ric Bastien and\n                        Xing Shi and\n                        Jan Schl\\\"{u}ter and\n                        Brian Thomas and\n                        Chris Capdevila and\n                        Alex Rubinsteyn and \n                        Michael M. Forbes and\n                        Jacob Frelinger and \n                        Tim Klein and\n                        Bruce Merry and\n                        Nate Merill and\n                        Lars Pastewka and\n                        Li Yong Liu and\n                        S. Clarkson and\n                        Michael Rader and\n                        Steve Taylor and\n                        Arnaud Bergeron and\n                        Nikul H. Ukani and\n                        Feng Wang and\n                        Wing-Kit Lee and\n                        Yiyin Zhou},\n        title        = {scikit-cuda 0.5.3: a {Python} interface to {GPU}-powered libraries},\n        month        = May,\n        year         = 2019,\n        doi          = {10.5281/zenodo.3229433},\n        url          = {http://dx.doi.org/10.5281/zenodo.3229433},\n        note         = {\\url{http://dx.doi.org/10.5281/zenodo.3229433}}\n    }\n\nAuthors & Acknowledgments\n-------------------------\nSee the included `AUTHORS\n<https://github.com/lebedov/scikit-cuda/blob/master/docs/source/authors.rst>`_\nfile for more information.\n\nNote Regarding CULA Availability\n--------------------------------\nAs of 2017, the CULA toolkit is available to premium tier users of\n`Celerity Tools <http://www.celeritytools.com>`_ (EM Photonics' new\nHPC site).\n\nRelated\n-------\nPython wrappers for `cuDNN <https://developer.nvidia.com/cudnn>`_ by Hannes \nBretschneider are available `here\n<https://github.com/hannes-brt/cudnn-python-wrappers>`_.\n\n`ArrayFire <https://github.com/arrayfire/arrayfire>`_ is a free library containing many GPU-based routines with an `officially supported Python interface <https://github.com/arrayfire/arrayfire-python>`_.\n\nLicense\n-------\nThis software is licensed under the `BSD License\n<http://www.opensource.org/licenses/bsd-license.php>`_.  See the included\n`LICENSE\n<https://github.com/lebedov/scikit-cuda/blob/master/docs/source/license.rst>`_\nfile for more information.\n\n\n",
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