cuGraph: GPU accelerated graph analytics

cuGraph is a library implementing Graph Analytics functionalities based on GPU Data Frames. For more project details, see rapids.ai.

Development Setup

The following instructions are tested on Linux systems.

Compiler requirement:

g++ 4.8 or 5.4
cmake 3.12+

CUDA requirement:

CUDA 9.0+

You can obtain CUDA from https://developer.nvidia.com/cuda-downloads.

Conda

You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.

Note: This conda installation only applies to Linux and Python versions 3.5/3.6.

You can create and activate a development environment using the conda commands:

# create the conda environment (assuming in base `cugraph` directory)
conda env create --name cugraph_dev --file conda/environments/dev_py35.yml
# activate the environment
source activate

The environment can be updated as development includes/changes the depedencies. To do so, run:

conda env update --name cugraph_dev --file conda/environments/dev_py35.yml
source activate

This installs the required cmake, cudf, pyarrow and other dependencies into the cugraph_dev conda environment and activates it.

Configure and build

This project uses cmake for building the C/C++ library. To configure cmake, run:

mkdir build   # create build directory for out-of-source build
cd build      # enter the build directory
cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DNVG_PLUGIN=FALSE  # configure cmake ... use $CONDA_PREFIX if you're using Anaconda

Add -DNVG_PLUGIN=TRUE to configure cmake to build nvGraph plugin for cuGraph.

To build the C/C++ code

make          #This should produce a shared library named `libcugraph.so`
make install  #The default locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/cugraph` respectively.

Install the Python package to your Python path:

python setup.py install    # install cudf python bindings

Run tests

C++ stand alone tests

From the build directory : gtests/gdfgraph_test

Python tests with datasets

From cugraph's directory :

tar -zxvf src/tests/datasets.tar.gz -C /    # tests will look for this 'datasets' folder in '/'
pytest

Documentation

Python API documentation can be generated from docs directory.

rgsl888 / cugraph