.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "howto/howto_examples/run_pyAFQ_with_GPU.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_howto_howto_examples_run_pyAFQ_with_GPU.py>`
        to download the full example code.

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

.. _sphx_glr_howto_howto_examples_run_pyAFQ_with_GPU.py:


============================================
Running pyAFQ using the GPU for tractography
============================================
Running pyAFQ using the GPU for tractography is as simple as
(1) Installing GPUStreamlines using `pip install` and
(2) passing in the ``tractography_ngpus`` parameter when you create your
    GroupAFQ object.
To install GPUStreamlines, do:
    `pip install git+https://github.com/dipy/GPUStreamlines.git`
That's step 1 complete! The rest of this example is the same as the GroupAFQ
example except with the ``tractography_ngpus`` parameter set.

.. GENERATED FROM PYTHON SOURCE LINES 14-20

.. code-block:: Python


    from AFQ.api.group import GroupAFQ
    import AFQ.data.fetch as afd
    import os.path as op
    import plotly


.. GENERATED FROM PYTHON SOURCE LINES 21-26

We start with some example data. The data we will use here is
generated from the
`Stanford HARDI dataset <https://purl.stanford.edu/ng782rw8378>`_.
We then setup our myafq object which we will use to demonstrate
the clobber method.

.. GENERATED FROM PYTHON SOURCE LINES 26-34

.. code-block:: Python


    afd.organize_stanford_data()

    tracking_params = dict(n_seeds=1000000,
                           random_seeds=True,
                           rng_seed=2025,
                           trx=True)


.. GENERATED FROM PYTHON SOURCE LINES 35-40

Running with the GPU
--------------------
We will use the GPU for tractography. This is done by
passing in `tractography_ngpus`
That's it!

.. GENERATED FROM PYTHON SOURCE LINES 40-49

.. code-block:: Python

    myafq = GroupAFQ(
        bids_path=op.join(afd.afq_home, 'stanford_hardi'),
        preproc_pipeline='vistasoft',
        tracking_params=tracking_params,
        tractography_ngpus=1)

    # From here, pyAFQ should run normally
    bundle_html = myafq.export("all_bundles_figure")
    plotly.io.show(bundle_html["01"][0])

**Estimated memory usage:**  0 MB


.. _sphx_glr_download_howto_howto_examples_run_pyAFQ_with_GPU.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: run_pyAFQ_with_GPU.zip <run_pyAFQ_with_GPU.zip>`


.. only:: html

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

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