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CONTRIBUTING.md

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Contributing to cuGraph

If you are interested in contributing to cuGraph, your contributions will fall into three categories:

  1. You want to report a bug, feature request, or documentation issue
    • File an issue describing what you encountered or what you want to see changed.
    • The RAPIDS team will evaluate the issues and triage them, scheduling them for a release. If you believe the issue needs priority attention comment on the issue to notify the team.
  2. You want to propose a new Feature and implement it
    • Post about your intended feature, and we shall discuss the design and implementation.
    • Once we agree that the plan looks good, go ahead and implement it, using the code contributions guide below.
  3. You want to implement a feature or bug-fix for an outstanding issue
    • Follow the code contributions guide below.
    • If you need more context on a particular issue, please ask and we shall provide.

Code contributions

Your first issue

  1. Read the project's README.md to learn how to setup the development environment
  2. Find an issue to work on. The best way is to look for the good first issue or help wanted labels
  3. Comment on the issue saying you are going to work on it
  4. Fork the cuGraph repo and Code! Make sure to update unit tests!
  5. When done, create your pull request
  6. Verify that CI passes all status checks. Fix if needed
  7. Wait for other developers to review your code and update code as needed
  8. Once reviewed and approved, a RAPIDS developer will merge your pull request

Remember, if you are unsure about anything, don't hesitate to comment on issues and ask for clarifications!

Seasoned developers

Once you have gotten your feet wet and are more comfortable with the code, you can look at the prioritized issues of our next release in our project boards.

Pro Tip: Always look at the release board with the highest number for issues to work on. This is where RAPIDS developers also focus their efforts.

Look at the unassigned issues, and find an issue you are comfortable with contributing to. Start with Step 3 from above, commenting on the issue to let others know you are working on it. If you have any questions related to the implementation of the issue, ask them in the issue instead of the PR.

Setting Up Your Build Environment

Build from Source

The following instructions are for developers and contributors to cuGraph OSS development. These instructions are tested on Linux Ubuntu 16.04 & 18.04. Use these instructions to build cuGraph from source and contribute to its development. Other operating systems may be compatible, but are not currently tested.

The cuGraph package include both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly.

The following instructions are tested on Linux systems.

Prerequisites

Compiler requirement:

  • gcc version 5.4+
  • nvcc version 9.2
  • cmake version 3.12

CUDA requirement:

  • CUDA 9.2+
  • NVIDIA driver 396.44+
  • Pascal architecture or better

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

Since cmake will download and build Apache Arrow you may need to install Boost C++ (version 1.58+) before running cmake:

# Install Boost C++ for Ubuntu 16.04/18.04
$ sudo apt-get install libboost-all-dev

or

# Install Boost C++ for Conda
$ conda install -c conda-forge boost

Build and Install the C/C++ CUDA components

To install cuGraph from source, ensure the dependencies are met and follow the steps below:

  1. Clone the repository and submodules
# Set the localtion to cuGraph in an environment variable CUGRAPH_HOME 
export CUGRAPH_HOME=$(pwd)/cugraph

# Download the cuGraph repo
git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME

# Next load all the submodules
cd $CUGRAPH_HOME
git submodule update --init --recursive
  1. Create the conda development environment
# create the conda environment (assuming in base `cugraph` directory)
# for CUDA 9.2
conda env create --name cugraph_dev --file conda/environments/cugraph_dev.yml

# for CUDA 10
conda env create --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.yml

# activate the environment
conda activate cugraph_dev 

# to deactivate an environment
conda deactivate
  • The environment can be updated as development includes/changes the dependencies. To do so, run:
# for CUDA 9.2
conda env update --name cugraph_dev --file conda/environments/cugraph_dev.yml

# for CUDA 10
conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda10.yml

conda activate cugraph_dev
  1. Build and install libcugraph. CMake depends on the nvcc executable being on your path or defined in $CUDACXX.

This project uses cmake for building the C/C++ library. CMake will also automatically build and install nvGraph library ($CUGRAPH_HOME/cpp/nvgraph) which may take a few minutes. To configure cmake, run:

# Set the localtion to cuGraph in an environment variable CUGRAPH_HOME 
export CUGRAPH_HOME=$(pwd)/cugraph

cd $CUGRAPH_HOME
cd cpp	      		# enter cpp directory
mkdir build   		# create build directory 
cd build     		# enter the build directory
cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX 

# now build the code
make -j				# "-j" starts multiple threads
make install		# install the libraries

The default installation locations are $CMAKE_INSTALL_PREFIX/lib and $CMAKE_INSTALL_PREFIX/include/cugraph respectively.

Building and installing the Python package

  1. Install the Python package to your Python path:
cd $CUGRAPH_HOME
cd python
python setup.py install    # install cugraph python bindings

Run tests

  1. Run either the C++ or the Python tests with datasets

  • Python tests with datasets

    cd $CUGRAPH_HOME
cd python
pytest

  • C++ stand alone tests

    From the build directory :

    # Run the cugraph tests
cd $CUGRAPH_HOME
cd cpp/build
gtests/GDFGRAPH_TEST		# this is an executable file

  • C++ tests with larger datasets

    If you already have the datasets:

    export RAPIDS_DATASET_ROOT_DIR=<path_to_ccp_test_and_reference_data>

    If you do not have the datasets:

    cd $CUGRAPH_HOME/datasets
source get_test_data.sh #This takes about 10 minutes and download 1GB data (>5 GB uncompressed)

    Run the C++ tests on large input:

    cd $CUGRAPH_HOME/cpp/build
#test one particular analytics (eg. pagerank)
gtests/PAGERANK_TEST
#test everything
make test

Note: This conda installation only applies to Linux and Python versions 3.6/3.7.

Building and Testing on a gpuCI image locally

Before submitting a pull request, you can do a local build and test on your machine that mimics our gpuCI environment using the ci/local/build.sh script. For detailed information on usage of this script, see here.

(OPTIONAL) Set environment variable on activation

It is possible to configure the conda environment to set environmental variables on activation. Providing instructions to set PATH to include the CUDA toolkit bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be helpful.

cd  ~/anaconda3/envs/cugraph_dev

mkdir -p ./etc/conda/activate.d
mkdir -p ./etc/conda/deactivate.d
touch ./etc/conda/activate.d/env_vars.sh
touch ./etc/conda/deactivate.d/env_vars.sh

Next the env_vars.sh file needs to be edited

vi ./etc/conda/activate.d/env_vars.sh

#!/bin/bash
export PATH=/usr/local/cuda-10.0/bin:$PATH # or cuda-9.2 if using CUDA 9.2
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH # or cuda-9.2 if using CUDA 9.2
vi ./etc/conda/deactivate.d/env_vars.sh

#!/bin/bash
unset PATH
unset LD_LIBRARY_PATH

Creating documentation

Python API documentation can be generated from docs directory.

C++ ABI issues

cuGraph builds with C++14 features. By default, we build cuGraph with the latest ABI (the ABI changed with C++11). The version of cuDF pointed to in the conda installation above is build with the new ABI.

If you see link errors indicating trouble finding functions that use C++ strings when trying to build cuGraph you may have an ABI incompatibility.

There are a couple of complications that may make this a problem:

  • if you need to link in a library built with the old ABI, you may need to build the entire tool chain from source using the old ABI.
  • if you build cudf from source (for whatever reason), the default behavior for cudf (at least through version 0.5.x) is to build using the old ABI. You can build with the new ABI, but you need to follow the instructions in CUDF to explicitly turn that on.

If you must build cugraph with the old ABI, you can use the following command (instead of the cmake call above):

cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DCMAKE_CXX11_ABI=OFF

Attribution

Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md