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

    Click :ref:`here <sphx_glr_download_auto_examples_example_2_local_parallel_threads.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_example_2_local_parallel_threads.py:


Example 2 - Local and Parallel (using threads)
==============================================

This example extends example 1 to multiple parallel workers.
For demonstrational purposes, the workers are started in separate threads.
This is probably not the most common use case but serves as a nice example.
The setup can be useful if the workers do their computations outside Python's Global Interpreter Lock, e.g. in some 3rd party extension in C/C++.
In that case, all workers can truely work in parallel.




.. code-block:: python

    import logging
    logging.basicConfig(level=logging.WARNING)

    import argparse

    import hpbandster.core.nameserver as hpns
    import hpbandster.core.result as hpres

    from hpbandster.optimizers import BOHB as BOHB
    from hpbandster.examples.commons import MyWorker



    parser = argparse.ArgumentParser(description='Example 1 - sequential and local execution.')
    parser.add_argument('--min_budget',   type=float, help='Minimum budget used during the optimization.',    default=9)
    parser.add_argument('--max_budget',   type=float, help='Maximum budget used during the optimization.',    default=243)
    parser.add_argument('--n_iterations', type=int,   help='Number of iterations performed by the optimizer', default=4)
    parser.add_argument('--n_workers', type=int,   help='Number of workers to run in parallel.', default=2)

    args=parser.parse_args()


    # Step 1: Start a nameserver (see example_1)
    NS = hpns.NameServer(run_id='example2', host='127.0.0.1', port=None)
    NS.start()

    # Step 2: Start the workers
    # Now we can instantiate the specified number of workers. To emphasize the effect,
    # we introduce a sleep_interval of one second, which makes every function evaluation
    # take a bit of time. Note the additional id argument that helps separating the
    # individual workers. This is necessary because every worker uses its processes
    # ID which is the same for all threads here.
    workers=[]
    for i in range(args.n_workers):
    	w = MyWorker(sleep_interval = 0.5, nameserver='127.0.0.1',run_id='example2', id=i)
    	w.run(background=True)
    	workers.append(w)

    # Step 3: Run an optimizer
    # Now we can create an optimizer object and start the run.
    # We add the min_n_workers argument to the run methods to make the optimizer wait
    # for all workers to start. This is not mandatory, and workers can be added
    # at any time, but if the timing of the run is essential, this can be used to
    # synchronize all workers right at the start.
    bohb = BOHB(  configspace = w.get_configspace(),
                  run_id = 'example2',
                  min_budget=args.min_budget, max_budget=args.max_budget
               )
    res = bohb.run(n_iterations=args.n_iterations, min_n_workers=args.n_workers)

    # Step 4: Shutdown
    # After the optimizer run, we must shutdown the master and the nameserver.
    bohb.shutdown(shutdown_workers=True)
    NS.shutdown()

    # Step 5: Analysis
    # Each optimizer returns a hpbandster.core.result.Result object.
    # It holds informations about the optimization run like the incumbent (=best) configuration.
    # For further details about the Result object, see its documentation.
    # Here we simply print out the best config and some statistics about the performed runs.
    id2config = res.get_id2config_mapping()
    incumbent = res.get_incumbent_id()

    all_runs = res.get_all_runs()

    print('Best found configuration:', id2config[incumbent]['config'])
    print('A total of %i unique configurations where sampled.' % len(id2config.keys()))
    print('A total of %i runs where executed.' % len(res.get_all_runs()))
    print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget))
    print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget))
    print('The run took  %.1f seconds to complete.'%(all_runs[-1].time_stamps['finished'] - all_runs[0].time_stamps['started']))

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_example_2_local_parallel_threads.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: example_2_local_parallel_threads.py <example_2_local_parallel_threads.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: example_2_local_parallel_threads.ipynb <example_2_local_parallel_threads.ipynb>`


.. only:: html

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

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