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

.. only:: html

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

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

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

.. _sphx_glr_examples_pipelines_kfp_intro_pipeline.py:


Intro KubeFlow Pipelines Example
================================

This an introductory pipeline using KubeFlow Pipelines built with only TorchX
components.

TorchX is intended to allow making cross platform components. As such, we have
a standard definition that uses adapters to convert it to the specific
pipeline platform. This is an example of using the KFP adapter to run a TorchX
component as part of a KubeFlow Pipeline.

TorchX tries to leverage standard mechanisms wherever possible. For KFP we use
the existing KFP pipeline definition syntax and add a single
`component_from_app` conversion step to convert a TorchX component into one
KFP can understand.

Typically you have a separate component file but for this example we define the
AppDef inline.

.. GENERATED FROM PYTHON SOURCE LINES 28-53

.. code-block:: default


    import kfp
    from torchx import specs
    from torchx.components.base.binary_component import binary_component
    from torchx.pipelines.kfp.adapter import container_from_app


    def pipeline() -> None:
        # First we define our AppDef for the component. We use the binary_component
        # helper for making single node components. AppDef is a core part of TorchX
        # and can be used to describe complex distributed multi container apps or
        # just a single node component like here.
        echo_app: specs.AppDef = binary_component(
            name="examples-intro",
            entrypoint="/bin/echo",
            args=["Hello TorchX!"],
            image="alpine",
        )

        # To convert the TorchX AppDef into a KFP container we use
        # the container_from_app adapter. This takes generates a KFP component
        # definition from the TorchX app def and instantiates it into a container.
        echo_container: kfp.dsl.ContainerOp = container_from_app(echo_app)



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To generate the pipeline definition file we need to call into the KFP compiler
with our pipeline function.

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.. code-block:: default


    kfp.compiler.Compiler().compile(
        pipeline_func=pipeline,
        package_path="pipeline.yaml",
    )

    with open("pipeline.yaml", 'rt') as f:
        print(f.read())


.. GENERATED FROM PYTHON SOURCE LINES 66-73

Once this has all run you should have a pipeline file (typically
pipeline.yaml) that you can upload to your KFP cluster via the UI or
a kfp.Client.

See the
`KFP SDK Examples <https://www.kubeflow.org/docs/components/pipelines/tutorials/sdk-examples/#examples>`_
for more info on launching KFP pipelines.

.. GENERATED FROM PYTHON SOURCE LINES 75-77

See the :ref:`Advanced KubeFlow Pipelines Example` for how to chain multiple
components together and use builtin components.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_pipelines_kfp_intro_pipeline.py:


.. only :: html

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



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

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



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

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


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

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