RAFT Build and Development Guide

Building and installing RAFT
Using RAFT in downstream projects

Building and installing RAFT

CUDA/GPU Requirements

CUDA Toolkit 11.0+
NVIDIA driver 450.80.02+
Pascal architecture of better (compute capability >= 6.0)

Build Dependencies

In addition to the libraries included with cudatoolkit 11.0+, there are some other dependencies below for building RAFT from source. Many of the dependencies are optional and depend only on the primitives being used. All of these can be installed with cmake or rapids-cpm and many of them can be installed with conda.

Required

RMM corresponding to RAFT version.

Optional

Thrust v1.15 / CUB - On by default but can be disabled.
cuCollections - Used in raft::sparse::distance API.
Libcu++ v1.7.0
FAISS v1.7.0 - Used in raft::spatial::knn API and needed to build tests.
NCCL - Used in raft::comms API and needed to build raft-dask
UCX - Used in raft::comms API and needed to build raft-dask
Googletest - Needed to build tests
Googlebench - Needed to build benchmarks
Doxygen - Needed to build docs

C++ RAFT is a header-only library but provides the option of building shared libraries with template instantiations for common types to speed up compile times for larger projects.

The recommended way to build and install RAFT is to use the build.sh script in the root of the repository. This script can build both the C++ and Python artifacts and provides options for building and installing the headers, tests, benchmarks, and individual shared libraries.

Header-only C++

build.sh uses rapids-cmake, which will automatically download any dependencies which are not already installed. It's important to note that while all the headers will be installed and available, some parts of the RAFT API depend on libraries like FAISS, which will need to be explicitly enabled in build.sh.

The following example will download the needed dependencies and install the RAFT headers into $INSTALL_PREFIX/include/raft. The --install flag can be omitted to just have the build download the needed dependencies. Since RAFT is primarily used at build-time, the dependencies will never be installed by the RAFT build, with the exception of building FAISS statically into the shared libraries.

./build.sh libraft --install

C++ Shared Libraries (optional)

For larger projects which make heavy use of the pairwise distances or nearest neighbors APIs, shared libraries can be built to speed up compile times. These shared libraries can also significantly improve re-compile times both while developing RAFT and developing against the APIs. Build all of the available shared libraries by passing --compile-libs flag to build.sh:

./build.sh libraft --compile-libs

Individual shared libraries have their own flags and multiple can be used (though currently only the nn and distance packages contain shared libraries):

./build.sh libraft --compile-nn --compile-dist

Add the --install flag to the above example to also install the shared libraries into $INSTALL_PREFIX/lib.

`ccache` and `sccache`

ccache and sccache can be used to better cache parts of the build when rebuilding frequently, such as when working on a new feature. You can also use ccache or sccache with build.sh:

./build.sh libraft --cache-tool=ccache

Tests

Compile the tests using the tests target in build.sh.

./build.sh libraft tests

Test compile times can be improved significantly by using the optional shared libraries. If installed, they will be used automatically when building the tests but --compile-libs can be used to add additional compilation units and compile them with the tests.

./build.sh libraft tests --compile-libs

The tests are broken apart by algorithm category, so you will find several binaries in cpp/build/ named *_TEST.

For example, to run the distance tests:

./cpp/build/DISTANCE_TEST

It can take sometime to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the --limit-tests option in build.sh:

./build.sh libraft tests --limit-tests=NEIGHBORS_TEST;DISTANCE_TEST;MATRIX_TEST

Benchmarks

The benchmarks are broken apart by algorithm category, so you will find several binaries in cpp/build/ named *_BENCH.

./build.sh libraft bench

It can take sometime to compile all of the benchmarks. You can build individual benchmarks by providing a semicolon-separated list to the --limit-bench option in build.sh:

./build.sh libraft bench --limit-bench=NEIGHBORS_BENCH;DISTANCE_BENCH;LINALG_BENCH

C++ Using Cmake

Use CMAKE_INSTALL_PREFIX to install RAFT into a specific location. The snippet below will install it into the current conda environment:

cd cpp
mkdir build
cd build
cmake -D BUILD_TESTS=ON -DRAFT_COMPILE_LIBRARIES=ON -DRAFT_ENABLE_NN_DEPENDENCIES=ON  -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ../
make -j<parallel_level> install

RAFT's cmake has the following configurable flags available:.

Flag	Possible Values	Default Value	Behavior
BUILD_TESTS	ON, OFF	ON	Compile Googletests
BUILD_BENCH	ON, OFF	ON	Compile benchmarks
raft_FIND_COMPONENTS	nn distance		Configures the optional components as a space-separated list
RAFT_COMPILE_LIBRARIES	ON, OFF	OFF	Compiles all `libraft` shared libraries (these are required for Googletests)
RAFT_COMPILE_NN_LIBRARY	ON, OFF	OFF	Compiles the `libraft-nn` shared library
RAFT_COMPILE_DIST_LIBRARY	ON, OFF	OFF	Compiles the `libraft-distance` shared library
RAFT_ENABLE_NN_DEPENDENCIES	ON, OFF	OFF	Searches for dependencies of nearest neighbors API, such as FAISS, and compiles them if not found. Needed for `raft::spatial::knn`
RAFT_ENABLE_thrust_DEPENDENCY	ON, OFF	ON	Enables the Thrust dependency. This can be disabled when using many simple utilities or to override with a different Thrust version.
RAFT_USE_FAISS_STATIC	ON, OFF	OFF	Statically link FAISS into `libraft-nn`
RAFT_STATIC_LINK_LIBRARIES	ON, OFF	ON	Build static link libraries instead of shared libraries
DETECT_CONDA_ENV	ON, OFF	ON	Enable detection of conda environment for dependencies
NVTX	ON, OFF	OFF	Enable NVTX Markers
CUDA_ENABLE_KERNELINFO	ON, OFF	OFF	Enables `kernelinfo` in nvcc. This is useful for `compute-sanitizer`
CUDA_ENABLE_LINEINFO	ON, OFF	OFF	Enable the -lineinfo option for nvcc
CUDA_STATIC_RUNTIME	ON, OFF	OFF	Statically link the CUDA runtime

Currently, shared libraries are provided for the libraft-nn and libraft-distance components. The libraft-nn component depends upon FAISS and the RAFT_ENABLE_NN_DEPENDENCIES option will build it from source if it is not already installed.

Python

Conda environment scripts are provided for installing the necessary dependencies for building and using the Python APIs. It is preferred to use mamba, as it provides significant speedup over conda. In addition you will have to manually install nvcc as it will not be installed as part of the conda environment. The following example will install create and install dependencies for a CUDA 11.5 conda environment:

mamba env create --name raft_env_name -f conda/environments/raft_dev_cuda11.5.yml
mamba activate raft_env_name

The Python APIs can be built and installed using the build.sh script:

# to build pylibraft
./build.sh libraft pylibraft --install --compile-libs
# to build raft-dask
./build.sh libraft raft-dask --install --compile-libs

setup.py can also be used to build the Python APIs manually:

cd python/raft-dask
python setup.py build_ext --inplace
python setup.py install

cd python/pylibraft
python setup.py build_ext --inplace
python setup.py install

To run the Python tests:

cd python/raft-dask
py.test -s -v

cd python/pylibraft
py.test -s -v

Documentation

The documentation requires that the C++ headers and python packages have been built and installed.

The following will build the docs along with the C++ and Python packages:

./build.sh libraft pylibraft raft-dask docs --compile-libs --install

Using RAFT in downstream projects

There are two different strategies for including RAFT in downstream projects, depending on whether or not the required dependencies are already installed and available on the lib and include paths.

C++ header-only integration using cmake

When the needed build dependencies are already satisfied, RAFT can be trivially integrated into downstream projects by cloning the repository and adding cpp/include from RAFT to the include path:

set(RAFT_GIT_DIR ${CMAKE_CURRENT_BINARY_DIR}/raft CACHE STRING "Path to RAFT repo")
ExternalProject_Add(raft
  GIT_REPOSITORY    git@github.com:rapidsai/raft.git
  GIT_TAG           branch-22.10
  PREFIX            ${RAFT_GIT_DIR}
  CONFIGURE_COMMAND ""
  BUILD_COMMAND     ""
  INSTALL_COMMAND   "")
set(RAFT_INCLUDE_DIR ${RAFT_GIT_DIR}/raft/cpp/include CACHE STRING "RAFT include variable")

If RAFT has already been installed, such as by using the build.sh script, use find_package(raft) and the raft::raft target if using RAFT to interact only with the public APIs of consuming projects.

Using pre-compiled shared libraries

Use find_package(raft COMPONENTS nn distance) to enable the shared libraries and transitively pass dependencies through separate targets for each component. In this example, the raft::distance and raft::nn targets will be available for configuring linking paths in addition to raft::raft. These targets will also pass through any transitive dependencies (such as FAISS for the nn package).

The pre-compiled libraries contain template specializations for commonly used types, such as single- and double-precision floating-point. In order to use the symbols in the pre-compiled libraries, the compiler needs to be told not to instantiate templates that are already contained in the shared libraries. By convention, these header files are named specializations.hpp and located in the base directory for the packages that contain specializations.

The following example tells the compiler to ignore the pre-compiled templates for the libraft-distance API so any symbols already compiled into pre-compiled shared library will be used instead:

#include <raft/distance/distance.cuh>
#include <raft/distance/specializations.cuh>

Building RAFT C++ from source in cmake

RAFT uses the RAPIDS-CMake library so it can be more easily included into downstream projects. RAPIDS cmake provides a convenience layer around the CMake Package Manager (CPM).

The following example is similar to invoking find_package(raft) but uses rapids_cpm_find, which provides a richer and more flexible configuration landscape by using CPM to fetch any dependencies not already available to the build. The raft::raft link target will be made available and it's recommended that it be used as a PRIVATE link dependency in downstream projects. The COMPILE_LIBRARIES option enables the building the shared libraries.

The following cmake snippet enables a flexible configuration of RAFT:

set(RAFT_VERSION "22.10")
set(RAFT_FORK "rapidsai")
set(RAFT_PINNED_TAG "branch-${RAFT_VERSION}")

function(find_and_configure_raft)
  set(oneValueArgs VERSION FORK PINNED_TAG USE_FAISS_STATIC
          COMPILE_LIBRARIES ENABLE_NN_DEPENDENCIES CLONE_ON_PIN
          USE_NN_LIBRARY USE_DISTANCE_LIBRARY
          ENABLE_thrust_DEPENDENCY)
  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
                            "${multiValueArgs}" ${ARGN} )

  #-----------------------------------------------------
  # Clone RAFT locally if PINNED_TAG has been changed
  #-----------------------------------------------------
  if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${RAFT_VERSION}")
    message("Pinned tag found: ${PKG_PINNED_TAG}. Cloning raft locally.")
    set(CPM_DOWNLOAD_raft ON)
    set(CMAKE_IGNORE_PATH "${CMAKE_INSTALL_PREFIX}/include/raft;${CMAKE_IGNORE_PATH})
  endif()

  #-----------------------------------------------------
  # Add components
  #-----------------------------------------------------

  if(PKG_USE_NN_LIBRARY)
    string(APPEND RAFT_COMPONENTS " nn")
  endif()

  if(PKG_USE_DISTANCE_LIBRARY)
    string(APPEND RAFT_COMPONENTS " distance")
  endif()

  #-----------------------------------------------------
  # Invoke CPM find_package()
  #-----------------------------------------------------

  rapids_cpm_find(raft ${PKG_VERSION}
          GLOBAL_TARGETS      raft::raft
          BUILD_EXPORT_SET    projname-exports
          INSTALL_EXPORT_SET  projname-exports
          CPM_ARGS
          GIT_REPOSITORY https://github.com/${PKG_FORK}/raft.git
          GIT_TAG        ${PKG_PINNED_TAG}
          SOURCE_SUBDIR  cpp
          FIND_PACKAGE_ARGUMENTS "COMPONENTS ${RAFT_COMPONENTS}"
          OPTIONS
          "BUILD_TESTS OFF"
          "BUILD_BENCH OFF"
          "RAFT_ENABLE_NN_DEPENDENCIES ${PKG_ENABLE_NN_DEPENDENCIES}"
          "RAFT_USE_FAISS_STATIC ${PKG_USE_FAISS_STATIC}"
          "RAFT_COMPILE_LIBRARIES ${PKG_COMPILE_LIBRARIES}"
          "RAFT_ENABLE_thrust_DEPENDENCY ${PKG_ENABLE_thrust_DEPENDENCY}"
  )

endfunction()

# Change pinned tag here to test a commit in CI
# To use a different RAFT locally, set the CMake variable
# CPM_raft_SOURCE=/path/to/local/raft
find_and_configure_raft(VERSION    ${RAFT_VERSION}.00
        FORK             ${RAFT_FORK}
        PINNED_TAG       ${RAFT_PINNED_TAG}

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

        COMPILE_LIBRARIES        NO
        USE_NN_LIBRARY           NO
        USE_DISTANCE_LIBRARY     NO
        ENABLE_NN_DEPENDENCIES   NO  # This builds FAISS if not installed
        USE_FAISS_STATIC         NO
        ENABLE_thrust_DEPENDENCY YES
)

If using the nearest neighbors APIs without the shared libraries, set ENABLE_NN_DEPENDENCIES=ON and keep USE_NN_LIBRARY=OFF

Python/Cython Integration

Once installed, RAFT's Python library can be added to downstream conda recipes, imported and used directly.

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BUILD.md

BUILD.md

RAFT Build and Development Guide

Building and installing RAFT

CUDA/GPU Requirements

Build Dependencies

Required

Optional

Header-only C++

C++ Shared Libraries (optional)

`ccache` and `sccache`

Tests

Benchmarks

C++ Using Cmake

Python

Documentation

Using RAFT in downstream projects

C++ header-only integration using cmake

Using pre-compiled shared libraries

Building RAFT C++ from source in cmake

Python/Cython Integration

Files

BUILD.md

Latest commit

History

BUILD.md

File metadata and controls

RAFT Build and Development Guide

Building and installing RAFT

CUDA/GPU Requirements

Build Dependencies

Required

Optional

Header-only C++

C++ Shared Libraries (optional)

ccache and sccache

Tests

Benchmarks

C++ Using Cmake

Python

Documentation

Using RAFT in downstream projects

C++ header-only integration using cmake

Using pre-compiled shared libraries

Building RAFT C++ from source in cmake

Python/Cython Integration

`ccache` and `sccache`