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

.. only:: html

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

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

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

.. _sphx_glr_python_gpu-examples_cover_type.py:


Using xgboost on GPU devices
============================

Shows how to train a model on the `forest cover type
<https://archive.ics.uci.edu/ml/datasets/covertype>`_ dataset using GPU
acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it
time consuming to process. We compare the run-time and accuracy of the GPU and CPU
histogram algorithms.

In addition, The demo showcases using GPU with other GPU-related libraries including
cupy and cuml. These libraries are not strictly required.

.. GENERATED FROM PYTHON SOURCE LINES 15-50

.. code-block:: Python

    import time

    import cupy as cp
    from cuml.model_selection import train_test_split
    from sklearn.datasets import fetch_covtype

    import xgboost as xgb

    # Fetch dataset using sklearn
    X, y = fetch_covtype(return_X_y=True)
    X = cp.array(X)
    y = cp.array(y)
    y -= y.min()

    # Create 0.75/0.25 train/test split
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.25, train_size=0.75, random_state=42
    )

    # Specify sufficient boosting iterations to reach a minimum
    num_round = 3000

    # Leave most parameters as default
    clf = xgb.XGBClassifier(device="cuda", n_estimators=num_round)
    # Train model
    start = time.time()
    clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
    gpu_res = clf.evals_result()
    print("GPU Training Time: %s seconds" % (str(time.time() - start)))

    # Repeat for CPU algorithm
    clf = xgb.XGBClassifier(device="cpu", n_estimators=num_round)
    start = time.time()
    cpu_res = clf.evals_result()
    print("CPU Training Time: %s seconds" % (str(time.time() - start)))


.. _sphx_glr_download_python_gpu-examples_cover_type.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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