.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/60_search/example_parallel_n_jobs.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_60_search_example_parallel_n_jobs.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_examples_60_search_example_parallel_n_jobs.py:


===================================
Parallel Usage  on a single machine
===================================

*Auto-sklearn* uses
`dask.distributed <https://distributed.dask.org/en/latest/index.html`>_
for parallel optimization.

This example shows how to start *Auto-sklearn* to use multiple cores on a
single machine. Using this mode, *Auto-sklearn* starts a dask cluster,
manages the workers and takes care of shutting down the cluster once the
computation is done.
To run *Auto-sklearn* on multiple machines check the example
:ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py`.

.. GENERATED FROM PYTHON SOURCE LINES 18-26

.. code-block:: default


    import sklearn.model_selection
    import sklearn.datasets
    import sklearn.metrics

    import autosklearn.classification









.. GENERATED FROM PYTHON SOURCE LINES 27-29

Data Loading
============

.. GENERATED FROM PYTHON SOURCE LINES 29-34

.. code-block:: default

    X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
        X, y, random_state=1
    )








.. GENERATED FROM PYTHON SOURCE LINES 35-39

Build and fit a classifier
==========================

To use ``n_jobs_`` we must guard the code

.. GENERATED FROM PYTHON SOURCE LINES 39-55

.. code-block:: default

    if __name__ == "__main__":

        automl = autosklearn.classification.AutoSklearnClassifier(
            time_left_for_this_task=120,
            per_run_time_limit=30,
            tmp_folder="/tmp/autosklearn_parallel_1_example_tmp",
            n_jobs=4,
            # Each one of the 4 jobs is allocated 3GB
            memory_limit=3072,
            seed=5,
        )
        automl.fit(X_train, y_train, dataset_name="breast_cancer")

        # Print statistics about the auto-sklearn run such as number of
        # iterations, number of models failed with a time out.
        print(automl.sprint_statistics())




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


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    auto-sklearn results:
      Dataset name: breast_cancer
      Metric: accuracy
      Best validation score: 0.985816
      Number of target algorithm runs: 43
      Number of successful target algorithm runs: 42
      Number of crashed target algorithm runs: 0
      Number of target algorithms that exceeded the time limit: 1
      Number of target algorithms that exceeded the memory limit: 0






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 2 minutes  2.592 seconds)


.. _sphx_glr_download_examples_60_search_example_parallel_n_jobs.py:

.. only:: html

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


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/automl/auto-sklearn/development?urlpath=lab/tree/notebooks/examples/60_search/example_parallel_n_jobs.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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