raft-dask-cu12
Reusable Accelerated Functions & Tools Dask Infrastructure
Decision gist · record as of 2026-08-14
Yes, if you are building distributed GPU-accelerated applications on multi-node clusters and want to leverage RAFT's low-level primitives through Dask. The package is actively maintained, carries no known vulnerabilities, and is permissively licensed. Install friction is moderate due to CUDA dependencies, but this is expected for GPU-targeting libraries. Not recommended for single-node work or exploratory data science—use pylibraft-cu12 directly or RAPIDS cuML instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU with CUDA support and compatible CUDA 12 environment; intended for multi-node GPU clusters, not single-CPU systems.
- Medium install friction due to 6 runtime dependencies including CUDA-specific packages (libraft-cu12, nvidia-nccl-cu12, pylibraft-cu12) and Dask infrastructure.
- Actively maintained with recent releases; requires Python 3.11+.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for RAPIDS ecosystem components.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 1,037 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 186,345 downloads/mo, #9,990 on PyPI
Alternatives
Verify before relying
pip install raft-dask-cu12
from raft_dask_cu12 import ...
# Use with dask.distributed and GPU-accelerated RAFT primitives- Specific distributed algorithms and primitives exposed through raft-dask-cu12 Python API beyond the general RAFT categories mentioned.
- Whether this package is the recommended entry point for Dask users or if pylibraft-cu12 is preferred for single-node GPU work.
- Performance characteristics and scalability limits for typical multi-node multi-GPU deployments.
What it is and what it does
raft-dask-cu12 is the Dask integration layer for RAFT, enabling distributed GPU-accelerated computing across multiple nodes and GPUs. It wraps RAFT's host-accessible runtime APIs—which don't require a CUDA compiler—and layers them atop Dask's distributed task scheduler and NVIDIA's NCCL for inter-GPU communication. The package is part of the RAPIDS ecosystem and targets application developers and data source providers building high-performance distributed algorithms, not data scientists doing exploratory work.
The package depends on dask-cuda for GPU task scheduling, distributed-ucxx-cu12 for optimized communication, libraft-cu12 for the core accelerated primitives, and pylibraft-cu12 for Python bindings. It is actively maintained, supports Python 3.11 through 3.14, and carries no known security vulnerabilities. Installation requires a CUDA 12-compatible GPU environment and brings medium friction due to its compiled dependencies.
Use it for
- Build distributed machine learning algorithms that scale across GPU clusters using RAFT's low-level primitives as building blocks.
- Accelerate multi-node data processing workflows by offloading linear algebra, sparse operations, and statistical computations to GPUs.
- Integrate GPU-accelerated RAFT functions into existing Dask applications without rewriting core algorithms.
- Develop high-performance solvers and optimization routines that benefit from RAFT's centralized, reusable GPU kernels.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building distributed GPU-accelerated applications on multi-node clusters and want to leverage RAFT's low-level primitives through Dask.
The package is actively maintained, carries no known vulnerabilities, and is permissively licensed. Install friction is moderate due to CUDA dependencies, but this is expected for GPU-targeting libraries. Not recommended for single-node work or exploratory data science—use pylibraft-cu12 directly or RAPIDS cuML instead.
Install
raft-dask-cu12 on PyPI
Before you install
Medium install friction due to 6 runtime dependencies including CUDA-specific packages (libraft-cu12, nvidia-nccl-cu12, pylibraft-cu12) and Dask infrastructure. Actively maintained with recent releases; requires Python 3.11+.
Requires NVIDIA GPU with CUDA support and compatible CUDA 12 environment; intended for multi-node GPU clusters, not single-CPU systems.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for RAPIDS ecosystem components.
Quickstart
pip install raft-dask-cu12
from raft_dask_cu12 import ...
# Use with dask.distributed and GPU-accelerated RAFT primitivesVerify before relying
- Specific distributed algorithms and primitives exposed through raft-dask-cu12 Python API beyond the general RAFT categories mentioned.
- Whether this package is the recommended entry point for Dask users or if pylibraft-cu12 is preferred for single-node GPU work.
- Performance characteristics and scalability limits for typical multi-node multi-GPU deployments.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 6 packagesdask-cudadistributed-ucxx-cu12libraft-cu12nvidia-nccl-cu12pylibraft-cu12rapids-dask-dependency |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 186,345 / month, #9,990 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersProgramming Language :: PythonProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: raft_dask_cu12-26.8.0-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; raft_dask_cu12-26.8.0-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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See also libraft-cu12 · pylibraft-cu12 · dask-cuda · dask-cudf-cu12 · distributed-ucxx-cu12 · rapids-dask-dependency · rmm-cu12 · libcuml-cu12 · librmm-cu12 · libcudf-cu12