cuml.accel Overview#

cuml.accel runs supported scikit-learn, UMAP, and HDBSCAN workloads on an NVIDIA GPU without changing the Python code that uses those libraries. It is a good fit when you want to accelerate an existing workflow, keep the familiar APIs, or quickly evaluate the benefit of GPU acceleration before using cuML directly.

Enable cuml.accel before importing the libraries you want to accelerate. Your existing code then remains unchanged:

from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge

X, y = make_regression(n_samples=1_000_000, random_state=0)
model = Ridge().fit(X, y)
predictions = model.predict(X)

Run a script through the cuml.accel command-line interface:

python -m cuml.accel script.py

Or load the extension at the top of a Jupyter notebook, before other imports:

%load_ext cuml.accel

See Activation Methods for a complete overview of the command-line, Jupyter, environment-variable, and programmatic activation options.

cuml.accel transparently falls back to the original CPU implementation when an estimator or operation cannot be accelerated. Fallback can depend on parameters, input types, methods, or library versions, so existing workflows continue to run even when only part of a pipeline is GPU accelerated. Use the logging and profiling tools to see exactly where execution occurs.

We have validated cuml.accel against the complete scikit-learn example gallery: every runnable example completes successfully with cuml.accel enabled. This does not mean that every operation runs on the GPU. Unsupported operations transparently fall back to the CPU. The result demonstrates broad compatibility with realistic, complex scikit-learn workflows.

Where to Go Next#

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