libcuml-cu12

library26.6.0pypypiunverified

RAPIDS cuML (CUDA-accelerated Machine Learning) is a suite of GPU-accelerated machine learning libraries and algorithms designed to be fully compatible with scikit-learn APIs, enabling users to transition seamlessly from CPU to GPU without significant code changes. It's part of the broader RAPIDS ecosystem for data science, optimized for CUDA 12. The current version is 26.4.0, following a monthly release cadence aligned with the RAPIDS project.

ai-ml data

pip install libcuml-cu12

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · LIBCUML-CU12

libcuml-cu12

ai-mlpythonv26.6.0

Install

57.5s avg

Import

—

Disk

3763MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v26.2.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

2/6 runs

✓ 58.9s

py 3.11

2/6 runs

✓ 58s

py 3.12

2/6 runs

✓ 56.6s

py 3.13

2/6 runs

✓ 56.45s

py 3.9

2/6 runs

4/6 runs

3763MB installed

● package 3763MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

KMeans

✓ from cuml.cluster import KMeans

RandomForestClassifier

✓ from cuml.ensemble import RandomForestClassifier

LinearRegression

✓ from cuml.linear_model import LinearRegression

This example demonstrates how to perform k-means clustering using cuML. It generates synthetic data with scikit-learn, converts it to a cuDF DataFrame for GPU processing, and then fits a KMeans model to find clusters. It requires `cudf` and `scikit-learn`.

import cuml import cudf from sklearn.datasets import make_blobs # Generate synthetic data on CPU X, _ = make_blobs(n_samples=1000, n_features=10, centers=5, random_state=42) # Convert to cuDF DataFrame for GPU processing X_gdf = cudf.DataFrame(X) # Initialize and fit a cuML KMeans model kmeans = cuml.cluster.KMeans(n_clusters=5, random_state=42) kmeans.fit(X_gdf) # Predict cluster labels labels = kmeans.predict(X_gdf) print("Cluster labels (first 5):\n", labels.head()) print("Cluster centers (first 5 rows):\n", kmeans.cluster_centers_.head())

Debug

Known issues

breakingSparse input validation now raises `TypeError` if sparse input is not supported by the algorithm.

fix

Ensure that sparse input data is only provided to cuML algorithms that explicitly support it, or convert sparse data to a dense format before passing it to unsupported algorithms.

affects: >=26.04.00

breaking`check_is_fitted` validation and `feature_names_in_` support have been added, requiring estimators to conform more strictly to scikit-learn's API.

fix

If using custom or wrapped estimators, ensure they implement `_validate_data` and set `feature_names_in_` after fitting, consistent with scikit-learn's guidelines.

affects: >=26.04.00

breakingThe `handle` object has been deprecated from public APIs, affecting low-level GPU resource management.

fix

Review existing code for direct usage of `handle` objects and refactor to use higher-level cuML APIs. cuML now manages GPU resources internally for most use cases.

affects: >=26.02.00

breaking`output_type=None` in estimator `__init__` will no longer implicitly coerce to a global `output_type` setting.

fix

Explicitly specify `output_type` in the constructor for cuML estimators if you rely on a particular output type (e.g., `output_type='cudf'`).

affects: >=26.02.00

breaking`dask` is now an optional dependency. Dask-related features will require `dask` and `distributed` to be installed explicitly.

fix

If using cuML with Dask, ensure you install `dask` and `distributed` separately (e.g., `pip install dask distributed`).

affects: >=25.12.00

deprecatedThe `convert_to_*` methods (e.g., `convert_to_cudf`) in `cuml.ensemble` have been deprecated in favor of `as_*` methods (e.g., `as_cudf`).

fix

Update method calls to use the `as_*` prefix (e.g., `model.as_cudf(X)` instead of `model.convert_to_cudf(X)`).

affects: >=25.10.00

Upgrade

Version history

26.6.0latest on PyPI · released Jun 9, 2026

Audit

Dependencies

cuda-pythonrequiredRequired for CUDA interoperability.

numpyrequiredGeneral numerical computations.

pynvmlrequiredFor NVIDIA Management Library interaction.

rapids-dask-toolsrequiredUtilities for Dask integration.

scikit-learnrequiredFor API compatibility and sometimes used in conjunction.

scipyrequiredScientific computing utilities.

cudf-cu12optionalPractically essential for GPU DataFrame operations, which are the common input format for cuML.

daskoptionalFor distributed computing capabilities.

distributedoptionalDask scheduler and worker components for distributed computing.

Agent activity

6 hits · last 30 days

node

Resources

githubhttps://github.com/rapidsai/cuml ↗

packagehttps://pypi.org/project/libcuml-cu12/ ↗

homepagehttps://rapids.ai ↗