libcuml-cu12
cuML - RAPIDS ML Algorithms (C++)
Decision gist · record as of 2026-08-14
Yes, if you have NVIDIA GPU hardware and need to accelerate scikit-learn-style ML workflows. The active maintenance, permissive Apache-2.0 license, and scikit-learn API compatibility make it a low-risk choice. Install friction is moderate due to CUDA and GPU library dependencies, but that is inherent to GPU compute; no known vulnerabilities. Not suitable without GPU hardware.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU with CUDA support and cuda-toolkit installed; Python 3.11 minimum.
- Medium install friction due to CUDA toolkit and multiple NVIDIA GPU libraries (libcuvs-cu12, libraft-cu12, librmm-cu12, nvidia-nvjitlink-cu12) as runtime dependencies.
- Actively maintained with release 8 days old; requires Python 3.11 or higher.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for proprietary projects provided attribution and license text are included.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 5,251 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 405,616 downloads/mo, #6,902 on PyPI
Alternatives
Verify before relying
pip install libcuml-cu12
from libcuml-cu12 import cluster
# GPU-accelerated clustering with DBSCAN
dbscan = cluster.DBSCAN(eps=1.0, min_samples=1)
dbscan.fit(data)
labels = dbscan.labels_- Performance claim of 10-50x speedup versus CPU equivalents—exact conditions and dataset sizes not specified.
- Compatibility with scikit-learn version 1.6 or higher—whether this is a hard requirement or advisory.
- Multi-GPU and multi-node support scope—which algorithms fully support Dask distributed operation.
- Exact import paths and module structure for clustering, regression, and other algorithm categories.
What it is and what it does
libcuml-cu12 is the CUDA 12 variant of cuML, NVIDIA's GPU-accelerated machine learning library that mirrors scikit-learn's API for tabular ML tasks. It implements clustering, regression, classification, dimensionality reduction, time series, and preprocessing—all running on NVIDIA GPUs instead of CPU.
The library is designed for data scientists and engineers who want to accelerate traditional ML workflows without writing CUDA code. It depends on cuda-toolkit, libcuvs-cu12, libraft-cu12, librmm-cu12, nvidia-nvjitlink-cu12, and rapids-logger. Multi-GPU and multi-node operation is available via Dask for a growing set of algorithms. Models can be serialized with pickle or joblib for later inference, though deserialization from untrusted sources poses security risks.
Use it for
- Accelerate large-scale clustering tasks on GPU when CPU processing is too slow.
- Train and deploy regression or classification models on GPU with scikit-learn-compatible interface.
- Perform distributed nearest neighbors search across multiple GPUs using Dask.
- Run dimensionality reduction on large tabular datasets to prepare features for downstream tasks.
- Build time series forecasts on GPU-resident data without CPU bottlenecks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have NVIDIA GPU hardware and need to accelerate scikit-learn-style ML workflows.
The active maintenance, permissive Apache-2.0 license, and scikit-learn API compatibility make it a low-risk choice. Install friction is moderate due to CUDA and GPU library dependencies, but that is inherent to GPU compute; no known vulnerabilities. Not suitable without GPU hardware.
Install
libcuml-cu12 on PyPI
Before you install
Medium install friction due to CUDA toolkit and multiple NVIDIA GPU libraries (libcuvs-cu12, libraft-cu12, librmm-cu12, nvidia-nvjitlink-cu12) as runtime dependencies. Actively maintained with release 8 days old; requires Python 3.11 or higher.
Requires NVIDIA GPU with CUDA support and cuda-toolkit installed; Python 3.11 minimum.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for proprietary projects provided attribution and license text are included.
Quickstart
pip install libcuml-cu12
from libcuml-cu12 import cluster
# GPU-accelerated clustering with DBSCAN
dbscan = cluster.DBSCAN(eps=1.0, min_samples=1)
dbscan.fit(data)
labels = dbscan.labels_Verify before relying
- Performance claim of 10-50x speedup versus CPU equivalents—exact conditions and dataset sizes not specified.
- Compatibility with scikit-learn version 1.6 or higher—whether this is a hard requirement or advisory.
- Multi-GPU and multi-node support scope—which algorithms fully support Dask distributed operation.
- Exact import paths and module structure for clustering, regression, and other algorithm categories.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 7 packagescuda-toolkitlibcuvs-cu12libnvforest-cu12libraft-cu12librmm-cu12nvidia-nvjitlink-cu12rapids-logger |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 405,616 / month, #6,902 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: GPU :: NVIDIA CUDAIntended Audience :: DevelopersProgramming Language :: C++Topic :: Scientific/Engineering |
Evidence: libcuml_cu12-26.8.0-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; libcuml_cu12-26.8.0-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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See also cuml-cu12 · scikit-learn-intelex · libcuvs-cu12 · libraft-cu12 · cuvs-cu12 · raft-dask-cu12 · cudf-cu12 · lightgbm · faiss-gpu · libcudf-cu12