Installation

Conda

The easiest way to install RAFT is through conda and several packages are provided.

libraft-headers RAFT headers
libraft (optional) shared library containing pre-compiled template instantiations and runtime API.
pylibraft (optional) Python wrappers around RAFT algorithms and primitives.
raft-dask (optional) enables deployment of multi-node multi-GPU algorithms that use RAFT raft::comms in Dask clusters.

Use the following command, depending on your CUDA version, to install all of the RAFT packages with conda (replace rapidsai with rapidsai-nightly to install more up-to-date but less stable nightly packages). mamba is preferred over the conda command.

# for CUDA 11.8
mamba install -c rapidsai -c conda-forge -c nvidia raft-dask pylibraft cuda-version=11.8

# for CUDA 12.0
mamba install -c rapidsai -c conda-forge -c nvidia raft-dask pylibraft cuda-version=12.0

You can also install the conda packages individually using the mamba command above.

After installing RAFT, find_package(raft COMPONENTS nn distance) can be used in your CUDA/C++ cmake build to compile and/or link against needed dependencies in your raft target. COMPONENTS are optional and will depend on the packages installed.

Pip

pylibraft and raft-dask both have experimental packages that can be installed through pip:

pip install pylibraft-cu11 --extra-index-url=https://pypi.nvidia.com
pip install raft-dask-cu11 --extra-index-url=https://pypi.nvidia.com

Building and installing RAFT

CUDA/GPU Requirements

cmake 3.26.4+
GCC 9.3+ (9.5.0+ recommended)
CUDA Toolkit 11.2+
NVIDIA driver 450.80.02+
Pascal architecture or 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.
Thrust v1.17 / CUB
cuCollections - Used in raft::sparse::distance API.
CUTLASS v2.9.1 - Used in raft::distance API.

Optional

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

All of RAFT's C++ APIs can be used header-only but pre-compiled shared libraries also contain some host-accessible APIs and template instantiations to accelerate compile times.

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 CUTLASS, 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.

./build.sh libraft

The -n flag can be passed 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.

./build.sh libraft -n

Once installed, libraft headers (and dependencies which were downloaded and installed using rapids-cmake) can be uninstalled also using build.sh:

./build.sh libraft --uninstall

C++ Shared Library (optional)

A shared library can be built for speeding up compile times. The shared library also contains a runtime API that allows you to invoke RAFT APIs directly from C++ source files (without nvcc). The shared library can also significantly improve re-compile times both while developing RAFT and using its APIs to develop applications. Pass the --compile-lib flag to build.sh to build the library:

./build.sh libraft --compile-lib

In above example the shared library is installed by default into $INSTALL_PREFIX/lib. To disable this, pass -n flag.

Once installed, the shared library, headers (and any dependencies downloaded and installed via rapids-cmake) can be uninstalled using build.sh:

./build.sh libraft --uninstall

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-lib

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 -n --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 -n --limit-bench=NEIGHBORS_BENCH;DISTANCE_BENCH;LINALG_BENCH

C++ Using Cmake Directly

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_LIBRARY=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_PRIMS_BENCH	ON, OFF	OFF	Compile benchmarks
BUILD_ANN_BENCH	ON, OFF	OFF	Compile end-to-end ANN benchmarks
RAFT_COMPILE_LIBRARY	ON, OFF	ON if either BUILD_TESTS or BUILD_PRIMS_BENCH is ON; otherwise OFF	Compiles all `libraft` shared libraries (these are required for Googletests)
raft_FIND_COMPONENTS	compiled distributed		Configures the optional components as a space-separated list
RAFT_ENABLE_CUBLAS_DEPENDENCY	ON, OFF	ON	Link against cublas library in `raft::raft`
RAFT_ENABLE_CUSOLVER_DEPENDENCY	ON, OFF	ON	Link against cusolver library in `raft::raft`
RAFT_ENABLE_CUSPARSE_DEPENDENCY	ON, OFF	ON	Link against cusparse library in `raft::raft`
RAFT_ENABLE_CUSOLVER_DEPENDENCY	ON, OFF	ON	Link against curand library in `raft::raft`
DETECT_CONDA_ENV	ON, OFF	ON	Enable detection of conda environment for dependencies
RAFT_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.

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 11.8 conda environment:

mamba env create --name raft_env_name -f conda/environments/all_cuda-118_arch-x86_64.yaml
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 --compile-lib
# to build raft-dask
./build.sh libraft pylibraft raft-dask --compile-lib

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

The Python packages can also be uninstalled using the build.sh script:

./build.sh pylibraft raft-dask --uninstall

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-lib

Using RAFT in downstream projects

There are a few different strategies for including RAFT in downstream projects, depending on whether the required build dependencies have already been installed and are available on the lib and include paths.

Using cmake, you can enable CUDA support right in your project's declaration:

project(YOUR_PROJECT VERSION 0.1 LANGUAGES CXX CUDA)

Please note that some additional compiler flags might need to be added when building against RAFT. For example, if you see an error like this The experimental flag '--expt-relaxed-constexpr' can be used to allow this.. The necessary flags can be set with cmake:

target_compile_options(your_target_name PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--expt-extended-lambda --expt-relaxed-constexpr>)

Further, it's important that the language level be set to at least C++ 17. This can be done with cmake:

set_target_properties(your_target_name
PROPERTIES CXX_STANDARD                        17
           CXX_STANDARD_REQUIRED               ON
           CUDA_STANDARD                       17
           CUDA_STANDARD_REQUIRED              ON
           POSITION_INDEPENDENT_CODE           ON
           INTERFACE_POSITION_INDEPENDENT_CODE ON)

C++ header-only integration (without cmake)

While not a highly suggested method for building against RAFT, when all of the needed build dependencies are already satisfied, RAFT can be 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-23.10
  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")

C++ header-only integration (with cmake)

When using cmake, you can install RAFT headers into your environment with ./build.sh libraft.

If the RAFT headers have already been installed into your environment with cmake or through conda, such as by using the build.sh script, use find_package(raft) and the raft::raft target.

Using C++ pre-compiled shared libraries

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

The pre-compiled libraries contain template instantiations for commonly used types, such as single- and double-precision floating-point. By default, these are used automatically when the RAFT_COMPILED macro is defined during compilation. This definition is automatically added by CMake.

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_LIBRARY option enables the building the shared libraries.

The following cmake snippet enables a flexible configuration of RAFT:

set(RAFT_VERSION "23.10")
set(RAFT_FORK "rapidsai")
set(RAFT_PINNED_TAG "branch-${RAFT_VERSION}")

function(find_and_configure_raft)
  set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARY)
  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
                            "${multiValueArgs}" ${ARGN} )
  
  #-----------------------------------------------------
  # 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 compiled distributed"
          OPTIONS
          "BUILD_TESTS OFF"
          "BUILD_PRIMS_BENCH OFF"
          "BUILD_ANN_BENCH OFF"
          "RAFT_COMPILE_LIBRARY ${PKG_COMPILE_LIBRARY}"
  )

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}
        COMPILE_LIBRARY          NO
)

You can find a fully-functioning example template project in the cpp/template directory, which provides everything you need to build a new application with RAFT or incorporate RAFT Into your existing libraries.

Uninstall

Once built and installed, RAFT can be safely uninstalled using build.sh by specifying any or all of the installed components. Please note that since pylibraft depends on libraft, uninstalling pylibraft will also uninstall libraft:

./build.sh libraft pylibraft raft-dask --uninstall

Leaving off the installed components will uninstall everything that's been installed:

./build.sh --uninstall

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Installation

Conda

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Optional

Header-only C++

C++ Shared Library (optional)

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Benchmarks

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Python

Documentation

Using RAFT in downstream projects

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Installation

Conda

Pip

Building and installing RAFT

CUDA/GPU Requirements

Build Dependencies

Required

Optional

Header-only C++

C++ Shared Library (optional)

ccache and sccache

Tests

Benchmarks

C++ Using Cmake Directly

Python

Documentation

Using RAFT in downstream projects

C++ header-only integration (without cmake)

C++ header-only integration (with cmake)

Using C++ pre-compiled shared libraries

Building RAFT C++ from source in cmake

Uninstall