.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "python/dask-examples/sklearn_gpu_training.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_python_dask-examples_sklearn_gpu_training.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_python_dask-examples_sklearn_gpu_training.py:


Use scikit-learn regressor interface with GPU histogram tree method
===================================================================

.. GENERATED FROM PYTHON SOURCE LINES 5-47

.. code-block:: Python


    from dask import array as da
    from dask.distributed import Client

    # It's recommended to use dask_cuda for GPU assignment
    from dask_cuda import LocalCUDACluster

    from xgboost import dask as dxgb


    def main(client: Client) -> dxgb.Booster:
        # generate some random data for demonstration
        n = 100
        m = 1000000
        partition_size = 10000
        X = da.random.random((m, n), partition_size)
        y = da.random.random(m, partition_size)

        regressor = dxgb.DaskXGBRegressor(verbosity=1)
        # set the device to CUDA
        regressor.set_params(tree_method="hist", device="cuda")
        # assigning client here is optional
        regressor.client = client

        regressor.fit(X, y, eval_set=[(X, y)])
        prediction = regressor.predict(X)

        bst = regressor.get_booster()
        history = regressor.evals_result()

        print("Evaluation history:", history)
        # returned prediction is always a dask array.
        assert isinstance(prediction, da.Array)
        return bst  # returning the trained model


    if __name__ == "__main__":
        # With dask cuda, one can scale up XGBoost to arbitrary GPU clusters.
        # `LocalCUDACluster` used here is only for demonstration purpose.
        with LocalCUDACluster() as cluster:
            with Client(cluster) as client:
                main(client)


.. _sphx_glr_download_python_dask-examples_sklearn_gpu_training.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: sklearn_gpu_training.ipynb <sklearn_gpu_training.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: sklearn_gpu_training.py <sklearn_gpu_training.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: sklearn_gpu_training.zip <sklearn_gpu_training.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_