Fetch metadata during pipeline composition

Accessing meta information during pipeline composition.

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Fetch metadata during pipeline composition

Pipeline configuration using the PipelineContext

To find information about the pipeline configuration during pipeline composition, you can use the zenml.get_pipeline_context() function to access the PipelineContext of your pipeline:

from zenml import get_pipeline_context

...

@pipeline(
    extra={
        "complex_parameter": [
            ("sklearn.tree", "DecisionTreeClassifier"),
            ("sklearn.ensemble", "RandomForestClassifier"),
        ]
    }
)
def my_pipeline():
    context = get_pipeline_context()

    after = []
    search_steps_prefix = "hp_tuning_search_"
    for i, model_search_configuration in enumerate(
        context.extra["complex_parameter"]
    ):
        step_name = f"{search_steps_prefix}{i}"
        cross_validation(
            model_package=model_search_configuration[0],
            model_class=model_search_configuration[1],
            id=step_name
        )
        after.append(step_name)
    select_best_model(
        search_steps_prefix=search_steps_prefix, 
        after=after,
    )

See the API Docs for more information on which attributes and methods the PipelineContext provides.