.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "11_demos\python_packages\parallelization_threading\demoMultiProcessing.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_11_demos_python_packages_parallelization_threading_demoMultiProcessing.py>`
        to download the full example code

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

.. _sphx_glr_11_demos_python_packages_parallelization_threading_demoMultiProcessing.py:

Multi processing
===================

This is a modified example from the python documentation.
The only difference is the ``set_executable`` section at the start.
Please notice that you cannot use methods from the itom module
in any worker thread.
Alternative approaches for multiprocessing are 
python ``threading`` module and ``asyncio``. Or use ``subprocess``.

.. GENERATED FROM PYTHON SOURCE LINES 10-16

.. code-block:: default

    from multiprocessing import Pool, TimeoutError
    import multiprocessing
    import time
    import os



.. GENERATED FROM PYTHON SOURCE LINES 18-19

Demo function for parallelization. 

.. GENERATED FROM PYTHON SOURCE LINES 19-23

.. code-block:: default

    def func(x):
        print("return x*x with x = ", x)
        return x * x


.. GENERATED FROM PYTHON SOURCE LINES 24-25

Python executable is required 

.. GENERATED FROM PYTHON SOURCE LINES 25-67

.. code-block:: default

    pythonPath = ui.getOpenFileName(
        "Set path of python.exe",
        "C:/itom/3rdParty/Python/python.exe",
        "Python Executable (*.exe)",
    )

    if pythonPath and os.path.exists(pythonPath):
        # set the path of the python executable (embedded python is required)
        multiprocessing.set_executable(pythonPath)

        with Pool(processes=4) as pool:

            # print "[0, 1, 4,..., 81]"
            print(pool.map(func, range(10)))

            # print same numbers in arbitrary order
            for i in pool.imap_unordered(func, range(10)):
                print(i)

            # evaluate "f(20)" asynchronously
            res = pool.apply_async(func, (20,))  # runs in *only* one process
            print(res.get(timeout=1))  # prints "400"

            # evaluate "os.getpid()" asynchronously
            res = pool.apply_async(os.getpid, ())  # runs in *only* one process
            print(res.get(timeout=1))  # prints the PID of that process

            # launching multiple evaluations asynchronously *may* use more processes
            multiple_results = [pool.apply_async(os.getpid, ()) for i in range(4)]
            print([res.get(timeout=1) for res in multiple_results])

            # make a single worker sleep for 10 secs
            res = pool.apply_async(time.sleep, (10,))
            try:
                print(res.get(timeout=1))
            except TimeoutError:
                print("We lacked patience and got a multiprocessing.TimeoutError")

            print("For the moment, the pool remains available for more work")

        # exiting the 'with'-block has stopped the pool
        print("Now the pool is closed and no longer available")


.. _sphx_glr_download_11_demos_python_packages_parallelization_threading_demoMultiProcessing.py:

.. only:: html

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


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

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

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

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