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- GitHub repository: Download the RAFT source code.
- Issue tracker: Report issues or request features.
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 |
|---|---|
| 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 |
| 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 raft_dask_api.rst using_raft_comms.rst developer_guide.md contributing.md