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Discover top open-source software, updated regularly with real-world adoption signals.
Reusable CUDA-accelerated primitives for high-performance GPU ML
RAFT offers header-only CUDA-accelerated primitives and optional shared libraries, plus lightweight Python wrappers, to speed development of GPU-based machine-learning and data-mining applications.
RAFT is aimed at developers and engineers building custom GPU‑accelerated machine‑learning or data‑mining pipelines. It provides low‑level, reusable primitives that can be composed into higher‑level algorithms, making it ideal for library authors and performance‑focused teams.
The library is a C++ header‑only template collection with an optional shared library to reduce compile times. It includes host‑accessible runtime APIs, seamless integration with RAPIDS Memory Manager (RMM), and multi‑dimensional array abstractions (mdspan, mdarray). Python access is available through pylibraft and distributed GPU support via raft-dask. While RAFT once bundled vector‑search utilities, those have migrated to cuVS; users needing ANN should adopt cuVS directly.
Install RAFT from source or via RAPIDS package channels, link against RMM, and create a raft::device_resources handle to manage streams and library handles. Use the C++ primitives directly or call them from Python with pylibraft. For multi‑node workloads, integrate raft-dask with Dask clusters to scale across GPUs.
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Custom clustering algorithm
Leverage RAFT sparse operations and random blob generation to implement a GPU‑accelerated clustering pipeline.
High‑throughput linear algebra in simulations
Use RAFT dense matrix utilities to replace CPU BLAS calls, achieving significant speedups in scientific codes.
Python distance computation
Call `pylibraft` distance primitives from CuPy arrays to compute pairwise Euclidean distances without leaving the Python ecosystem.
Distributed GPU training with Dask
Integrate `raft-dask` to share resources and synchronize data across multiple GPU nodes during model training.
Core primitives are written in C++; lightweight Python wrappers are provided via `pylibraft` and `raft-dask`.
RAFT relies on RAPIDS Memory Manager (RMM) and offers `mdarray`/`mdspan` abstractions that handle allocation and deallocation automatically.
Yes, RAFT is designed to interoperate with cuDF, cuML, and other RAPIDS components.
Vector‑search and clustering have been moved to the dedicated cuVS library; use cuVS for those routines.
Install via conda or build from source following the RAFT documentation; ensure RMM is available in the environment.
Project at a glance
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