• At its simplest, this could just mean checking that all of the steps and the pipeline itself have completed successfully (by checking their “Task status”).If a pipeline step ends with "failed" status in the pipeline execution function an exception will be raised, if the exception is not caught, the pipeline itself will also fail

run

pipeline_script.py

which contains the pipeline code as decorators.

So in theory the following should actually work.
Let's assume you have pipeline_script.py` with:

...

@PipelineDecorator.pipeline(pipeline_execution_queue=None, name='custom pipeline logic', project='examples', version='0.0.5')
def executing_pipeline(model_ids, mock_parameter='mock'):
    accs = [test_model(i) for i in model_ids]
    # the casting will wait for the model comparison to complete, before we just launched the components
    accs = [int(c) for a in accs]
    print("best model is", max(accs))
    return Task.current_task()

def run_pipeline(model_ids):
  PipelineDecorator.set_default_execution_queue('default')
  return executing_pipeline(model_ids=model_ids)

Then from the git action you could do:

from pipeline_script import run_pipeline

task_id = run_pipeline(model_ids=['aa', 'bb', 'cc])
pipeline_task = Task.get_task(task_id)
# do some stuff here

Without getting into too much details this seems totally doable.
I suggest you play around with it, and if you feel something is missing, I would love to help solve / add new features to the pipeline. wdyt?