Building from Source#

These instructions are tested on supported versions/distributions of Linux, CUDA, and Python - See RAPIDS Getting Started for the list of supported environments. Other environments might be compatible, but are not currently tested.

Prerequisites#

Compilers:

gcc version 9.3+
nvcc version 11.5+

CUDA:

CUDA 11.2+
NVIDIA driver 450.80.02+
Pascal architecture or better

Further details and download links for these prerequisites are available on the RAPIDS System Requirements page.

Setting up the development environment#

Clone the repository:#

CUGRAPH_HOME=$(pwd)/cugraph
git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME
cd $CUGRAPH_HOME

Create the conda environment#

Using conda is the easiest way to install both the build and runtime dependencies for cugraph. While it is possible to build and run cugraph without conda, the required packages occasionally change, making it difficult to document here. The best way to see the current dependencies needed for a build and run environment is to examine the list of packages in the conda environment YAML files.

# for CUDA 11.x
conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml

# activate the environment
conda activate cugraph_dev

# to deactivate an environment
conda deactivate

The environment can be updated as cugraph adds/removes/updates its dependencies. To do so, run:

# for CUDA 11.x
conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml
conda activate cugraph_dev

Build and Install#

Build and install using `build.sh`#

Using the build.sh script, located in the $CUGRAPH_HOME directory, is the recommended way to build and install the cugraph libraries. By default, build.sh will build and install a predefined set of targets (packages/libraries), but can also accept a list of targets to build.

For example, to build only the cugraph C++ library (libcugraph) and the high-level python library (cugraph) without building the C++ test binaries, run this command:

$ cd $CUGRAPH_HOME
$ ./build.sh libcugraph pylibcugraph cugraph --skip_cpp_tests

There are several other options available on the build script for advanced users. Refer to the output of --help for details.

Note that libraries will be installed to the location set in $PREFIX if set (i.e. export PREFIX=/install/path), otherwise to $CONDA_PREFIX.

Updating the RAFT branch#

libcugraph uses the RAFT library and there are times when it might be desirable to build against a different RAFT branch, such as when working on new features that might span both RAFT and cuGraph.

For local development, the CPM_raft_SOURCE=<path/to/raft/source> option can be passed to the cmake command to enable libcugraph to use the local RAFT branch. The build.sh script calls cmake to build the C/C++ targets, but developers can call cmake directly in order to pass it options like those described here. Refer to the build.sh script to see how to call cmake and other commands directly.

To have CI test a cugraph pull request against a different RAFT branch, modify the bottom of the cpp/cmake/thirdparty/get_raft.cmake file as follows:

# Change pinned tag and fork here to test a commit in CI
# To use a different RAFT locally, set the CMake variable
# RPM_raft_SOURCE=/path/to/local/raft
find_and_configure_raft(VERSION    ${CUGRAPH_MIN_VERSION_raft}
                        FORK       <your_git_fork>
                        PINNED_TAG <your_git_branch_or_tag>

                        # When PINNED_TAG above doesn't match cugraph,
                        # force local raft clone in build directory
                        # even if it's already installed.
                        CLONE_ON_PIN     ON
                        )

When the above change is pushed to a pull request, the continuous integration servers will use the specified RAFT branch to run the cuGraph tests. After the changes in the RAFT branch are merged to the release branch, remember to revert the get_raft.cmake file back to the original cuGraph branch.

Run tests#

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 downloads 1GB data (>5 GB uncompressed)

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

Python tests with datasets

pip install python-louvain #some tests require this package to run
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

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 3.8/3.10.

(OPTIONAL) Set environment variable on activation#

It is possible to configure the conda environment to set environment 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-11.0/bin:$PATH # or cuda-11.1 if using CUDA 11.1 and cuda-11.2 if using CUDA 11.2, respectively
export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:$LD_LIBRARY_PATH # or cuda-11.1 if using CUDA 11.1 and cuda-11.2 if using CUDA 11.2, respectively

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/cugraph directory. Or through using “./build.sh docs”

Attribution#

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