RAPIDS RAFT: Reusable Accelerated Functions and Tools for Vector Search and More ================================================================================ .. image:: ../../img/raft-tech-stack-vss.png :width: 800 :alt: RAFT Tech Stack Useful Resources ################ .. _raft_reference: https://docs.rapids.ai/api/raft/stable/ - `Example Notebooks `_: Example Jupyter notebooks - `RAPIDS Community `_: Get help, contribute, and collaborate. - `GitHub repository `_: Download the RAFT source code. - `Issue tracker `_: Report issues or request features. What is RAFT? ############# 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 - accelerates algorithm construction time - reduces the maintenance burden by maximizing reuse across projects, and - centralizes core reusable computations, allowing future optimizations to benefit all algorithms that use them. While not exhaustive, the following general categories help summarize the accelerated building blocks that RAFT contains: .. list-table:: :widths: 25 50 :header-rows: 1 * - Category - Examples * - Nearest Neighbors - pairwise distances, vector search, epsilon neighborhoods, neighborhood graph construction * - Data Formats - sparse & dense, conversions, data generation * - Dense Operations - linear algebra, matrix and vector operations, slicing, norms, factorization, least squares, svd & eigenvalue problems * - Sparse Operations - linear algebra, eigenvalue problems, slicing, norms, reductions, factorization, symmetrization, components & labeling * - Basic Clustering - spectral clustering, hierarchical clustering, k-means * - Solvers - combinatorial optimization, iterative solvers * - Statistics - sampling, moments and summary statistics, metrics * - Tools & Utilities - common utilities for developing CUDA applications, multi-node multi-gpu infrastructure .. toctree:: :maxdepth: 1 :caption: Contents: quick_start.md build.md cpp_api.rst pylibraft_api.rst using_libraft.md vector_search_tutorial.md raft_ann_benchmarks.md raft_dask_api.rst using_raft_comms.rst developer_guide.md contributing.md Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`