diff --git a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md deleted file mode 100644 index 80c666d65..000000000 --- a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md +++ /dev/null @@ -1,48 +0,0 @@ - -# Getting the WholeGraph Packages - -Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) -and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. - - -There are 4 ways to get WholeGraph packages: -1. [Quick start with Docker Repo](#docker) -2. [Conda Installation](#conda) -3. [Pip Installation](#pip) -4. [Build from Source](./source_build.md) - - -
- -## Docker -The RAPIDS Docker containers (as of Release 23.10) contain all RAPIDS packages, including WholeGraph, as well as all required supporting packages. To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries. - -
- - -## Conda -It is easy to install WholeGraph using conda. You can get a minimal conda installation with [miniforge](https://github.com/conda-forge/miniforge). - -WholeGraph conda packages - * libwholegraph - * pylibwholegraph - -Replace the package name in the example below to the one you want to install. - - -Install and update WholeGraph using the conda command: - -```bash -conda install -c rapidsai -c conda-forge -c nvidia wholegraph cudatoolkit=11.8 -``` - -
- -## PIP -wholegraph, and all of RAPIDS, is available via pip. - -``` -pip install wholegraph-cu11 --extra-index-url=https://pypi.nvidia.com -``` - -
diff --git a/docs/cugraph/source/wholegraph/installation/source_build.md b/docs/cugraph/source/wholegraph/installation/source_build.md deleted file mode 100644 index 7213cbfb0..000000000 --- a/docs/cugraph/source/wholegraph/installation/source_build.md +++ /dev/null @@ -1,186 +0,0 @@ -# Building from Source - -The following instructions are for users wishing to build wholegraph from source code. These instructions are tested on supported distributions of Linux,CUDA, -and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for a list of supported environments. -Other operating systems _might be_ compatible, but are not currently tested. - -The wholegraph package includes both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. -The C/C++ CUDA library is `libwholegraph` and the python library is `pylibwholegraph`. - -## Prerequisites - -__Compiler__: -* `gcc` version 11.0+ -* `nvcc` version 11.0+ -* `cmake` version 3.26.4+ - -__CUDA__: -* CUDA 11.8+ -* Volta architecture or better - -You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). - -__Other Packages__: -* ninja -* nccl -* cython -* setuputils3 -* scikit-learn -* scikit-build-core -* nanobind>=0.2.0 - -## Building wholegraph -To install wholegraph from source, ensure the dependencies are met. - -### Clone Repo and Configure Conda Environment -__GIT clone a version of the repository__ - - ```bash - # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME - export WHOLEGRAPH_HOME=$(pwd)/wholegraph - - # Download the wholegraph repo - if you have a forked version, use that path here instead - git clone https://github.com/rapidsai/wholegraph.git $WHOLEGRAPH_HOME - - cd $WHOLEGRAPH_HOME - ``` - -__Create the conda development environment__ - -```bash -# create the conda environment (assuming in base `wholegraph` directory) - -# for CUDA 11.x -conda env create --name wholegraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml - -# activate the environment -conda activate wholegraph_dev - -# to deactivate an environment -conda deactivate -``` - - - The environment can be updated as development includes/changes the dependencies. To do so, run: - - -```bash - -# Where XXX is the CUDA version -conda env update --name wholegraph_dev --file conda/environments/all_cuda-XXX_arch-x86_64.yaml - -conda activate wholegraph_dev -``` - - -### Build and Install Using the `build.sh` Script -Using the `build.sh` script make compiling and installing wholegraph a -breeze. To build and install, simply do: - -```bash -$ cd $WHOLEGRAPH_HOME -$ ./build.sh clean -$ ./build.sh libwholegraph -$ ./build.sh pylibwholegraph -``` - -There are several other options available on the build script for advanced users. -`build.sh` options: -```bash -build.sh [ ...] [ ...] - where is: - clean - remove all existing build artifacts and configuration (start over). - uninstall - uninstall libwholegraph and pylibwholegraph from a prior build/install (see also -n) - libwholegraph - build the libwholegraph C++ library. - pylibwholegraph - build the pylibwholegraph Python package. - tests - build the C++ (OPG) tests. - benchmarks - build benchmarks. - docs - build the docs - and is: - -v - verbose build mode - -g - build for debug - -n - no install step - --allgpuarch - build for all supported GPU architectures - --cmake-args=\\\"\\\" - add arbitrary CMake arguments to any cmake call - --compile-cmd - only output compile commands (invoke CMake without build) - --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) - -h | --h[elp] - print this text - - default action (no args) is to build and install 'libwholegraph' then 'pylibwholegraph' targets - -examples: -$ ./build.sh clean # remove prior build artifacts (start over) -$ ./build.sh - -# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) -$ PARALLEL_LEVEL=4 ./build.sh libwholegraph - -Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. -``` - - -## Building each section independently -### Build and Install the C++/CUDA `libwholegraph` Library -CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. - -This project uses cmake for building the C/C++ library. To configure cmake, run: - - ```bash - # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME - export WHOLEGRAPH_HOME=$(pwd)/wholegraph - - cd $WHOLEGRAPH_HOME - cd cpp # enter cpp directory - mkdir build # create build directory - cd build # enter the build directory - cmake .. -DCMAKE_INSTALL_PREFIX=$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/wholegraph` respectively. - -### Building and installing the Python package - -Build and Install the Python packages to your Python path: - -```bash -cd $WHOLEGRAPH_HOME -cd python -cd pylibwholegraph -python setup.py build_ext --inplace -python setup.py install # install pylibwholegraph -``` - -## Run tests - -Run either the C++ or the Python tests with datasets - - - **Python tests with datasets** - - ```bash - cd $WHOLEGRAPH_HOME - cd python - pytest - ``` - - - **C++ stand alone tests** - - From the build directory : - - ```bash - # Run the tests - cd $WHOLEGRAPH_HOME - cd cpp/build - gtests/PARALLEL_UTILS_TESTS # this is an executable file - ``` - - -Note: This conda installation only applies to Linux and Python versions 3.10, 3.11, and 3.12. - -## Creating documentation - -Python API documentation can be generated from _./docs/wholegraph directory_. Or through using "./build.sh docs" - -## Attribution -Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md