.. _sphx_glr_auto_examples_plot_asynchronous.py:


Asynchronous Stochastic Gradient
================================

Comparison of asynchronous SAGA (ASAGA) for different number of cores.




.. image:: /auto_examples/images/sphx_glr_plot_asynchronous_001.png
    :align: center


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

 Out::

    Loading data ...
    RCV1 dataset is not present in data folder. Downloading it ...
    Finished downloading
    Running with 1 core ...
    Running with 2 cores ...




|


.. code-block:: python

    import numpy as np
    import pylab as plt
    import copt as cp

    print('Loading data ...')
    X, y = cp.datasets.load_rcv1()

    # .. use a logistic loss with L1 + L2 (aka elastic-net) penalty ..
    f = cp.LogisticLoss(X, y, 1.0 / X.shape[0])
    g = cp.L1Norm(1e-6)

    print('Running with 1 core ...')
    opt_1cores = cp.minimize_SAGA(f, trace=True, max_iter=20)

    print('Running with 2 cores ...')
    opt_2cores = cp.minimize_SAGA(f, trace=True, n_jobs=2, max_iter=20)

    # .. plot result ..
    fmin = 0.0844502390843
    plt.plot((opt_1cores.trace_func - fmin) / fmin, lw=4, marker='H',
             markersize=10, markevery=5, label='1 core')
    plt.plot((opt_2cores.trace_func - fmin) / fmin, lw=4, marker='^',
             markersize=10, markevery=5, label='2 cores')
    plt.yscale('log')
    plt.ylabel('Function suboptimality')
    plt.xlabel('Iterations (per core)')
    plt.xlim((0, 20))
    # plt.ylim(ymin=1e-10)
    plt.grid()
    plt.legend()
    plt.show()

**Total running time of the script:** ( 4 minutes  0.478 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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