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How to parallelize loops?
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In image processing, we frequently apply the same algorithm
on a large batch of images. Let us define an example.

.. code-block:: python

    from skimage import data, color
    from skimage.restoration import denoise_tv_chambolle
    from skimage.feature import hog

    def tasks(image):
        """
        Apply some functions.
        """
        image = denoise_tv_chambolle(image, weight=0.1, multichannel=True)
        fd, hog_image = hog(color.rgb2gray(image), orientations=8,
                            pixels_per_cell=(16, 16), cells_per_block=(1, 1),
                            visualise=True)


    # Prepare images
    hubble = data.hubble_deep_field()
    width = 10
    pics = [hubble[:,slice:slice+width] for slice in range(0, 1000, width)]

To call the function ``tasks`` on each element of the list ``pics``, it is
usual to write a for loop. To measure the execution time of this loop, a function
is defined and called with ``timeit``.

.. code-block:: python

    def classic_loop():
        for image in pics:
            tasks(image)

    import timeit
    print("classic_loop():", timeit.timeit("classic_loop()", setup="from __main__ import (classic_loop, tasks, pics)", number=1))

Another equivalent way to code this loop is to use a comprehension list which has the same efficiency.

.. code-block:: python

    def comprehension_loop():
        [tasks(image) for image in pics]

    print("comprehension_loop():", timeit.timeit("comprehension_loop()", setup="from __main__ import (comprehension_loop, tasks, pics)", number=1))

``joblib`` is a library providing an easy way to parallelize for loops once we have a comprehension list.
The number of jobs can be specified.

.. code-block:: python

    from joblib import Parallel, delayed
    def joblib_loop():
        Parallel(n_jobs=4)(delayed(tasks)(i) for i in pics)

    print("joblib_loop():", timeit.timeit("joblib_loop()", setup="from __main__ import (joblib_loop, tasks, pics)", number=1))