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 Copy Chapter

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, time series, model
explanation, and nearest-neighbor workflows. Browse the API
reference for the current list of
estimators and functions.

Accelerate existing code with `cuml.accel` Copy Chapter

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 Copy Chapter

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 Copy Chapter

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 Copy Chapter

See the build guide.

Scikit-learn compatibility Copy Chapter

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

Model serialization and security Copy Chapter

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 Copy Chapter

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 Copy Chapter

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}
}