oneAPI and GPU support in Intel(R) Extension for Scikit-learn*ΒΆ

daal4py is installed with Intel(R) Extension for Scikit-learn* and provides support of oneAPI concepts, such as context and queues, which means that algorithms can be executed on different devices, GPUs in particular. This is implemented via with sycl_context("xpu") blocks that redirect execution to a device of the selected type: GPU, CPU, or host. Same approach is implemented for Intel(R) Extension for Scikit-learn*, so scikit-learn programs can be executed on GPU devices as well.

To patch your code with Intel CPU/GPU optimizations:

from sklearnex import patch_sklearn
from daal4py.oneapi import sycl_context
patch_sklearn()

from sklearn.cluster import DBSCAN

X = np.array([[1., 2.], [2., 2.], [2., 3.],
            [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
with sycl_context("gpu"):
   clustering = DBSCAN(eps=3, min_samples=2).fit(X)

For execution on GPU, DPC++ compiler runtime and driver are required. Refer to DPC++ system requirements for details.

DPC++ compiler runtime can be installed either from PyPI or Anaconda:

  • Install from PyPI:

    pip install dpcpp-cpp-rt
    
  • Install from Anaconda:

    conda install dpcpp_cpp_rt -c intel