[!IMPORTANT] The vector search and clustering algorithms in RAFT are being migrated to a new library dedicated to vector search called cuVS. We will continue to support the vector search algorithms in RAFT during this move, but will no longer update them after the RAPIDS 24.06 (June) release. We plan to complete the migration by RAPIDS 24.08 (August) release.
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
By taking a primitives-based approach to algorithm development, RAFT
While not exhaustive, the following general categories help summarize the accelerated functions in RAFT:
Category | Accelerated Functions in RAFT |
---|---|
Nearest Neighbors | vector search, neighborhood graph construction, epsilon neighborhoods, pairwise distances |
Basic Clustering | spectral clustering, hierarchical clustering, k-means |
Solvers | combinatorial optimization, iterative solvers |
Data Formats | sparse & dense, conversions, data generation |
Dense Operations | linear algebra, matrix and vector operations, reductions, slicing, norms, factorization, least squares, svd & eigenvalue problems |
Sparse Operations | linear algebra, eigenvalue problems, slicing, norms, reductions, factorization, symmetrization, components & labeling |
Statistics | sampling, moments and summary statistics, metrics, model evaluation |
Tools & Utilities | common tools and utilities for developing CUDA applications, multi-node multi-gpu infrastructure |
RAFT is a C++ header-only template library with an optional shared library that
In addition being a C++ library, RAFT also provides 2 Python libraries:
pylibraft
- lightweight Python wrappers around RAFT's host-accessible "runtime" APIs.raft-dask
- multi-node multi-GPU communicator infrastructure for building distributed algorithms on the GPU with Dask.RAFT contains state-of-the-art implementations of approximate nearest neighbors search (ANNS) algorithms on the GPU, such as:
Projects that use the RAFT ANNS algorithms for accelerating vector search include: Milvus, Redis, and Faiss.
Please see the example Jupyter notebook to get started RAFT for vector search in Python.
RAFT contains a catalog of reusable primitives for composing algorithms that require fast neighborhood computations, such as
RAFT's primitives are used in several RAPIDS libraries, including cuML, cuGraph, and cuOpt to build many end-to-end machine learning algorithms that span a large spectrum of different applications, including
RAFT is also used by the popular collaborative filtering library implicit for recommender systems.
RAFT contains low-level primitives for accelerating applications and workflows. Data source providers and application developers may find specific tools -- like ANN algorithms -- very useful. RAFT is not intended to be used directly by data scientists for discovery and experimentation. For data science tools, please see the RAPIDS website.
RAFT relies heavily on RMM which eases the burden of configuring different allocation strategies globally across the libraries that use it.
The APIs in RAFT accept the mdspan multi-dimensional array view for representing data in higher dimensions similar to the ndarray
in the Numpy Python library. RAFT also contains the corresponding owning mdarray
structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory.
The mdarray
forms a convenience layer over RMM and can be constructed in RAFT using a number of different helper functions:
#include <raft/core/device_mdarray.hpp> int n_rows = 10; int n_cols = 10; auto scalar = raft::make_device_scalar<float>(handle, 1.0); auto vector = raft::make_device_vector<float>(handle, n_cols); auto matrix = raft::make_device_matrix<float>(handle, n_rows, n_cols);
Most of the primitives in RAFT accept a raft::device_resources
object for the management of resources which are expensive to create, such CUDA streams, stream pools, and handles to other CUDA libraries like cublas
and cusolver
.
The example below demonstrates creating a RAFT handle and using it with device_matrix
and device_vector
to allocate memory, generating random clusters, and computing
pairwise Euclidean distances:
#include <raft/core/device_resources.hpp> #include <raft/core/device_mdarray.hpp> #include <raft/random/make_blobs.cuh> #include <raft/distance/distance.cuh> raft::device_resources handle; int n_samples = 5000; int n_features = 50; auto input = raft::make_device_matrix<float, int>(handle, n_samples, n_features); auto labels = raft::make_device_vector<int, int>(handle, n_samples); auto output = raft::make_device_matrix<float, int>(handle, n_samples, n_samples); raft::random::make_blobs(handle, input.view(), labels.view()); auto metric = raft::distance::DistanceType::L2SqrtExpanded; raft::distance::pairwise_distance(handle, input.view(), input.view(), output.view(), metric);
It's also possible to create raft::device_mdspan
views to invoke the same API with raw pointers and shape information:
#include <raft/core/device_resources.hpp> #include <raft/core/device_mdspan.hpp> #include <raft/random/make_blobs.cuh> #include <raft/distance/distance.cuh> raft::device_resources handle; int n_samples = 5000; int n_features = 50; float *input; int *labels; float *output; ... // Allocate input, labels, and output pointers ... auto input_view = raft::make_device_matrix_view(input, n_samples, n_features); auto labels_view = raft::make_device_vector_view(labels, n_samples); auto output_view = raft::make_device_matrix_view(output, n_samples, n_samples); raft::random::make_blobs(handle, input_view, labels_view); auto metric = raft::distance::DistanceType::L2SqrtExpanded; raft::distance::pairwise_distance(handle, input_view, input_view, output_view, metric);
The pylibraft
package contains a Python API for RAFT algorithms and primitives. pylibraft
integrates nicely into other libraries by being very lightweight with minimal dependencies and accepting any object that supports the __cuda_array_interface__
, such as CuPy's ndarray. The number of RAFT algorithms exposed in this package is continuing to grow from release to release.
The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays. Note that CuPy is not a required dependency for pylibraft
.
import cupy as cp from pylibraft.distance import pairwise_distance n_samples = 5000 n_features = 50 in1 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32) in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32) output = pairwise_distance(in1, in2, metric="euclidean")
The output
array in the above example is of type raft.common.device_ndarray
, which supports cuda_array_interface making it interoperable with other libraries like CuPy, Numba, PyTorch and RAPIDS cuDF that also support it. CuPy supports DLPack, which also enables zero-copy conversion from raft.common.device_ndarray
to JAX and Tensorflow.
Below is an example of converting the output pylibraft.device_ndarray
to a CuPy array:
cupy_array = cp.asarray(output)
And converting to a PyTorch tensor:
import torch torch_tensor = torch.as_tensor(output, device='cuda')
Or converting to a RAPIDS cuDF dataframe:
cudf_dataframe = cudf.DataFrame(output)
When the corresponding library has been installed and available in your environment, this conversion can also be done automatically by all RAFT compute APIs by setting a global configuration option:
import pylibraft.config pylibraft.config.set_output_as("cupy") # All compute APIs will return cupy arrays pylibraft.config.set_output_as("torch") # All compute APIs will return torch tensors
You can also specify a callable
that accepts a pylibraft.common.device_ndarray
and performs a custom conversion. The following example converts all output to numpy
arrays:
pylibraft.config.set_output_as(lambda device_ndarray: return device_ndarray.copy_to_host())
pylibraft
also supports writing to a pre-allocated output array so any __cuda_array_interface__
supported array can be written to in-place:
import cupy as cp from pylibraft.distance import pairwise_distance n_samples = 5000 n_features = 50 in1 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32) in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32) output = cp.empty((n_samples, n_samples), dtype=cp.float32) pairwise_distance(in1, in2, out=output, metric="euclidean")
RAFT's C++ and Python libraries can both be installed through Conda and the Python libraries through Pip.
The easiest way to install RAFT is through conda and several packages are provided.
libraft-headers
C++ headerslibraft
(optional) C++ shared library containing pre-compiled template instantiations and runtime API.pylibraft
(optional) Python libraryraft-dask
(optional) Python library for deployment of multi-node multi-GPU algorithms that use the RAFT raft::comms
abstraction layer in Dask clusters.raft-ann-bench
(optional) Benchmarking tool for easily producing benchmarks that compare RAFT's vector search algorithms against other state-of-the-art implementations.raft-ann-bench-cpu
(optional) Reproducible benchmarking tool similar to above, but doesn't require CUDA to be installed on the machine. Can be used to test in environments with competitive CPUs.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 12.5 mamba install -c rapidsai -c conda-forge -c nvidia raft-dask pylibraft cuda-version=12.5
Note that the above commands will also install libraft-headers
and libraft
.
You can also install the conda packages individually using the mamba
command above. For example, if you'd like to install RAFT's headers and pre-compiled shared library to use in your project:
# for CUDA 12.5 mamba install -c rapidsai -c conda-forge -c nvidia libraft libraft-headers cuda-version=12.5
If installing the C++ APIs please see using libraft for more information on using the pre-compiled shared library. You can also refer to the example C++ template project for a ready-to-go CMake configuration that you can drop into your project and build against installed RAFT development artifacts above.
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
These packages statically build RAFT's pre-compiled instantiations and so the C++ headers and pre-compiled shared library won't be readily available to use in your code.
The build instructions contain more details on building RAFT from source and including it in downstream projects. You can also find a more comprehensive version of the above CPM code snippet the Building RAFT C++ and Python from source section of the build instructions.
You can find an example RAFT project template in the cpp/template
directory, which demonstrates how to build a new application with RAFT or incorporate RAFT into an existing CMake project.
If you are interested in contributing to the RAFT project, please read our Contributing guidelines. Refer to the Developer Guide for details on the developer guidelines, workflows, and principals.
When citing RAFT generally, please consider referencing this Github project.
@misc{rapidsai, title={Rapidsai/raft: RAFT contains fundamental widely-used algorithms and primitives for data science, Graph and machine learning.}, url={https://github.com/rapidsai/raft}, journal={GitHub}, publisher={Nvidia RAPIDS}, author={Rapidsai}, year={2022} }
If citing the sparse pairwise distances API, please consider using the following bibtex:
@article{nolet2021semiring,
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