represents a reference to future data that will be passed to the pipeline or produced by a task.
Your pipeline function should have parameters, so that they can later be configured in the Kubeflow Pipelines UI.
When your pipeline function is called, each function argument will be a
You can pass those objects to the components as arguments to instantiate them and create tasks.
PipelineParam can also represent an intermediate value that you pass between pipeline tasks.
Each task has outputs and you can get references to them from the
The task output references can again be passed to other components as arguments.
In most cases you do not need to construct
PipelineParam objects manually.
The following code sample shows how to define a pipeline with parameters:
@kfp.dsl.pipeline( name='My pipeline', description='My machine learning pipeline' ) def my_pipeline( my_num: int = 1000, my_name: str = 'some text', my_url: str = 'http://example.com' ): ... # In the pipeline function body you can use the `my_num`, `my_name`, # `my_url` arguments as PipelineParam objects.
For more information, you can refer to the guide on building components and pipelines.
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