from clearml import PipelineDecorator

@PipelineDecorator.component(cache=True, execution_queue="default")
def step(size: int):
    import numpy as np
    return np.random.random(size=size)

@PipelineDecorator.pipeline(
    name='ingest',
    project='data processing',
    version='0.1'
)
def pipeline_logic(do_stuff: bool):
    if do_stuff:
        return step(size=42)

if __name__ == '__main__':
    # run the pipeline on the current machine, for local debugging
    # for scale-out, comment-out the following line (Make sure a
    # 'services' queue is available and serviced by a ClearML agent
    # running either in services mode or through K8S/Autoscaler)
    PipelineDecorator.run_locally()

    pipeline_logic(do_stuff=True)