.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "python/dask-examples/cpu_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_cpu_training.py>`
        to download the full example code.

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

.. _sphx_glr_python_dask-examples_cpu_training.py:


Example of training with Dask on CPU
====================================

.. GENERATED FROM PYTHON SOURCE LINES 6-51

.. code-block:: Python


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

    from xgboost import dask as dxgb
    from xgboost.dask import DaskDMatrix


    def main(client: Client) -> None:
        # generate some random data for demonstration
        m = 100000
        n = 100
        rng = da.random.default_rng(1)
        X = rng.normal(size=(m, n), chunks=(10000, -1))
        y = X.sum(axis=1)

        # DaskDMatrix acts like normal DMatrix, works as a proxy for local
        # DMatrix scatter around workers.
        dtrain = DaskDMatrix(client, X, y)

        # Use train method from xgboost.dask instead of xgboost.  This
        # distributed version of train returns a dictionary containing the
        # resulting booster and evaluation history obtained from
        # evaluation metrics.
        output = dxgb.train(
            client,
            {"verbosity": 1, "tree_method": "hist"},
            dtrain,
            num_boost_round=4,
            evals=[(dtrain, "train")],
        )
        bst = output["booster"]
        history = output["history"]

        # you can pass output directly into `predict` too.
        prediction = dxgb.predict(client, bst, dtrain)
        print("Evaluation history:", history)
        print("Error:", da.sqrt((prediction - y) ** 2).mean().compute())


    if __name__ == "__main__":
        # or use other clusters for scaling
        with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
            with Client(cluster) as client:
                main(client)


.. _sphx_glr_download_python_dask-examples_cpu_training.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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