.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "howto/parallel_processing.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end <sphx_glr_download_howto_parallel_processing.py>` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_howto_parallel_processing.py: =================== Parallel Processing =================== .. GENERATED FROM PYTHON SOURCE LINES 8-11 You should be able to treat models like any other function in terms of parallelization. This example shows using the built-in :mod:`multiprocessing` to do process-based parallelization of an explicit system. .. GENERATED FROM PYTHON SOURCE LINES 11-27 .. code-block:: Python from multiprocessing import Pool import condor class Model(condor.ExplicitSystem): x = input() output.y = -x**2 + 2*x + 1 with Pool(5) as p: models = p.map(Model, [1, 2, 3]) for model in models: print(model.input, model.output) .. rst-class:: sphx-glr-script-out .. code-block:: none ModelInput(x=1) ModelOutput(y=array([2.])) ModelInput(x=2) ModelOutput(y=array([1.])) ModelInput(x=3) ModelOutput(y=array([-2.])) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.240 seconds) .. _sphx_glr_download_howto_parallel_processing.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: parallel_processing.ipynb <parallel_processing.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: parallel_processing.py <parallel_processing.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: parallel_processing.zip <parallel_processing.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_