Metadata-Version: 1.1
Name: nglpy-cuda
Version: 0.1.1
Summary: TODO
Home-page: https://github.com/maljovec/nglpy_cuda
Author: Daniel Patrick Maljovec
Author-email: maljovec002@gmail.com
License: BSD license
Description: ==========
        nglpy-cuda
        ==========
        
        .. badges
        
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        .. end_badges
        
        .. logo
        
        .. image:: docs/_static/nglpycu.svg
            :align: center
            :alt: nglpycu
        
        .. end_logo
        
        .. introduction
        
        A reimplementation of the Neighborhood Graph Library
        (NGL_) developed by Carlos Correa and Peter Lindstrom that
        supports pruning a graph on the GPU. Developed as a
        replacement for nglpy_ where a CUDA-compatible GPU is
        available.
        
        .. _NGL: http://www.ngraph.org/
        
        .. _nglpy: https://github.com/maljovec/nglpy
        
        .. LONG_DESCRIPTION
        
        Given a set of arbitrarily arranged points in any dimension, this library is
        able to construct several different types of neighborhood graphs mainly focusing
        on empty region graph algorithms such as the beta skeleton family of graphs.
        
        Consider using an optimized approximate nearest neighbor library (see ann-benchmarks_
        for an updated list of algorithms and their relative performance) to construct the
        initial graph to be pruned, otherwise this library will rely on the exact k-nearest
        algorithm provided by scikit-learn_.
        
        .. _ann-benchmarks: http://ann-benchmarks.com/
        
        .. _scikit-learn: http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors
        
        .. END_LONG_DESCRIPTION
        
        .. end_introduction
        
        .. prerequisites
        
        Prerequisites
        =============
        
        Nvidia CUDA Toolkit (TODO: determine minimum version number) - tested on 9.1.
        
        Otherwise, all other python requirements can be installed via pip::
        
            pip install -r requirements.txt
        
        .. end_prerequisites
        
        .. install
        
        Installation
        ============
        There is an experimental package available on pip, however the prerequisite libraries are not specified correctly, so be sure you have numpy, scipy, sklearn, and faiss installed (subject to change).
        ::
            pip install nglpy_cuda
        
        .. end-install
        
        .. build
        
        Build
        =====
        
        Building the Python package
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        For now, don't install this yet, but set it up in development mode::
        
            python setup.py develop
        
        Run the test suite to verify it is able to make the CUDA calls without erroring::
        
            python setup.py test
        
        From here you should be ready to use the library. Only proceed below if you
        run into some install issues and want to try to at least build the shared
        library that you can use in C/C++ applications.
        
        Building and Testing the CUDA Library Separately
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Until I get this packaged appropriately, use the following command to compile the CUDA code::
        
            nvcc src/ngl_cuda.cu -I include/ --compiler-options "-fPIC" --shared -o libnglcu.so
        
        The CUDA API can then be tested with a small C++ example (TODO: provide small data file in repo for testing this next line)::
        
            g++ -L. -I include/ src/test.cpp -lnglcu -o test
            ./test -i <input file> -d <# of dimensions> -c <# of points> -n <neighbor edge file> -k <k neighbors to prune> -b <beta parameter> -p <shape descriptor> -s <discretization steps> -r <positive integer means use the relaxed version>
        
        .. end_build
        
        .. usage
        
        Usage
        =====
        
        The Python interface exposes the a Graph object that can be be iterated
        over its edges which produces a tuple where the first two values are the
        integer indices and the third value is the distance between the two
        points::
        
            import numpy as np
            import nglpy_cuda as ngl
        
            X = np.random.uniform(size=(10, 2))
            graph = ngl.Graph(X, relaxed=False)
        
            for edge in graph:
                print(edge)
        
        .. end-usage
        
        
        =======
        History
        =======
        
        0.1.0 (2018-07-15)
        ------------------
        
        * First release on PyPI.
        
Keywords: nglpy_cuda
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
