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 11.8+
NVIDIA GPU, Volta architecture or later, with compute capability 7.0+
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
# for CUDA 12.x
conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-125_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
# for CUDA 12.x
conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-125_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 $CUGRAPH_HOME/cpp/build #test one particular analytics (eg. pagerank) gtests/PAGERANK_TEST #test everything make test
Note: This conda installation only applies to Linux and Python versions 3.10 and 3.11.
(OPTIONAL) Set environment variable on activation#
It is possible to configure the conda environment to set environment variables on activation. Providing instructions to set PATH to include the CUDA toolkit bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be helpful.
cd ~/anaconda3/envs/cugraph_dev
mkdir -p ./etc/conda/activate.d
mkdir -p ./etc/conda/deactivate.d
touch ./etc/conda/activate.d/env_vars.sh
touch ./etc/conda/deactivate.d/env_vars.sh
Next the env_vars.sh file needs to be edited
vi ./etc/conda/activate.d/env_vars.sh
#!/bin/bash
export PATH=/usr/local/cuda-11.0/bin:$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