Zero Code Change Acceleration

The cuml.accel zero code change accelerator provides a mechanism to accelerate existing python machine learning code on the GPU, without requiring any changes to that code. Depending on the data size and algorithms chosen, this may result in major speedups.

%%load_ext cuml.accel
# Certain operations in common ML libraries (sklearn, umap, hdbscan)
# are now GPU accelerated

from sklearn.datasets import make_regression
from sklearn.linear_model import ElasticNet

X, y = make_regression(n_samples=1_000_000)

model = ElasticNet()
model.fit(X, y)   # runs on GPU!
model.predict(X)  # runs on GPU!

Currently cuml.accel targets sklearn, umap, and hdbscan as libraries to accelerate. Functionality that isn’t yet supported will fallback to CPU execution. See Limitations for more information on what’s currently accelerated and what requires a CPU fallback.

Usage

cuml.accel comes standard with cuml, no additional installation requirements are needed. It’s designed to be used with existing code that makes use of sklearn, umap or hdbscan, with the only change being something to enable the use of the accelerator.

Command Line Interface (CLI)

When executing from the commandline, you can use python -m cuml.accel in place of python to execute python code with the accelerator enabled.

python -m cuml.accel script.py

Jupyter/IPython

The same functionality is available in Jupyter notebooks or IPython by executing the following cell magic at the top (before other imports):

%%load_ext cuml.accel

You can see an example of this in this example.

Enabling Programmatically

When needed, the accelerator may also be enabled programmatically by calling cuml.accel.install. Note that you’ll want to call this early in your code, before importing functionality from sklearn/umap/hdbscan.

import cuml
cuml.accel.install()