RAPIDS RAFT: Reusable Accelerated Functions and Tools for Vector Search and More

Useful Resources

• 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:

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

Contents:

• Quick Start
• Installation
• C++ API
• Python API
• Using The Pre-Compiled Binary
• Vector Search in C++ Tutorial
• RAFT ANN Benchmarks
• RAFT Dask API
• Using RAFT Comms
• Developer Guide
• Contributing

Indices and tables

• Index

• Module Index

• Search Page