cuml

NVIDIA cuML: GPU-Accelerated Machine Learning

NVIDIA cuML is an open-source CUDA-X Data Science library for GPU-accelerated machine learning. It provides two ways to run machine learning workloads on NVIDIA GPUs:

• The cuml Python API provides GPU-native estimators with familiar scikit-learn-style APIs and direct control over machine learning workflows.
• cuml.accel accelerates existing scikit-learn, UMAP, and HDBSCAN code without changing the Python code that uses those libraries.

On representative benchmarks, cuML can accelerate scikit-learn workflows by up to 50x. Performance depends on the algorithm, dataset, and hardware. See the cuML benchmarks for results and methodology.

Use the GPU-native cuml API

The cuml Python API follows the familiar scikit-learn fit-predict-transform pattern while keeping data and computation on the GPU. The following example generates sample data and computes DBSCAN clusters on the GPU:

from cuml.datasets import make_blobs
from cuml.cluster import DBSCAN

# Create sample data
X, y = make_blobs(n_samples=100, centers=3, n_features=2, random_state=42)

# Fit clustering model
dbscan = DBSCAN(eps=1.0, min_samples=5)
dbscan.fit(X)
print(dbscan.labels_)

cuml supports clustering, dimensionality reduction, regression, classification, preprocessing, model selection, model explanation, and nearest-neighbor workflows. Browse the API reference for the current list of estimators and functions.

Accelerate existing code with cuml.accel

Run an existing Python script through the cuml.accel module:

python -m cuml.accel script.py

Or load the extension in a Jupyter notebook before importing scikit-learn, UMAP, or HDBSCAN:

%load_ext cuml.accel

Supported operations run on the GPU. When an estimator or configuration cannot be accelerated, cuml.accel uses the original CPU implementation so the rest of the workflow can continue. See the cuml.accel compatibility documentation for current coverage and fallback conditions. Use the logging and profiling tools to check which operations ran on the GPU.

Scale beyond one GPU

The cuml.dask API provides distributed implementations of selected algorithms for multi-GPU and multi-node execution with Dask. See the multi-GPU guide for cluster setup, supported algorithms, and examples.

Installation

Use the installation selector to generate a command for installing nightly or release cuML packages with conda, pip, or Docker.

Additional resources:

• NVIDIA cuML documentation
• NVIDIA cuML product page
• Walkthrough notebooks
• CUDA-X Data Science libraries

Build and install from source

See the build guide.

Scikit-learn compatibility

cuML is compatible with scikit-learn version 1.6 or higher.

Model serialization and security

cuML models can be serialized with pickle or joblib and loaded later for inference. cuML uses cloudpickle so that models trained with cuml.accel can be loaded and used with scikit-learn.

Only unpickle or deserialize from trusted sources. The pickle module (and by extension joblib) is not secure: malicious payloads can execute arbitrary code during deserialization and compromise your system. Do not unpickle or load data from untrusted or tampered sources. This applies to pickle.load(), pickle.loads(), joblib.load(), and any file-based model loading. For details and patterns, see the Model Serialization and Persistence notebook and the Python pickle security documentation.

Contributing and support

See the contributing guide to contribute to cuML. Report bugs and request features through GitHub issues. Join the broader community through the CUDA-X Data Science libraries page.

Citation

For additional details on the technologies behind cuML and the broader Python machine learning landscape, see Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (2020) by Sebastian Raschka, Joshua Patterson, and Corey Nolet.

Please consider citing this work when using cuML in a project:

@article{raschka2020machine,
  title={Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence},
  author={Raschka, Sebastian and Patterson, Joshua and Nolet, Corey},
  journal={arXiv preprint arXiv:2002.04803},
  year={2020}
}