if i put pipe.start earlier in the code, the pipeline fails to execute the actual steps.

pipe.start should be called after the pipeline was constructed and should be the "last" call of the script.
Not sure I follow what is "before" the code?

None
This seems like the same discussion , no ?

is it? I can't tell if these delays (DAG-computation) are pipeline-specific (i get that pipeline is just a type of task), but it felt like a different question as I'm asking "are pipelines like this appropriate?"

is there something fundamentally slower about using pipe.start() at the end of a pipeline vs pipe.run_locally() ?

pipe.start_locally() will run the DAG compute part on the same machine, where pipe.start() will start it on a remote worker (if it is not already running on a remote worker)
basically "pipe.start()" executed via an agent, will start the compute (no overhead)
does that help?

# imports

...

if __name__ == "__main__:

    pipe = PipelineController(...)

    # after instantiation, before "the code" that creates the pipeline.
    # normal tasks can handle task.execute_remotely() at this stage...
    pipe = add_steps_to_pipe(pipe)
    ...

    # after the pipeline is defined. best I can tell, *has* to be last thing in code.
    pipe.start_locally()  # or just .start()

i understood that part, but noticed that when putting in the code to start remotely, the consequence seems to be that the dag computation happens twice - once on my machine as it runs, and then again remotely (this is at least part of why its slower) . if i put pipe.start earlier in the code, the pipeline fails to execute the actual steps .

this is unlike tasks, which somehow are smart enough to publish in draft form when task.execute_remotely is up top .

do i just leave off pipe.start?

mind-blowing... but somehow just later in the same day I got the same pipeline to create its DAG and start running in under a minute.

I don't know what exactly I changed. The pipeline task was run locally (which I've never done before), then cloned to run remotely in my services queue. And then it just flew through the experiment at the pace I expected.

so there's hope. i'll keep stress-testing it and see what causes differences. I was right to suspect that such a simple DAG should not take upwards of 30-60 minutes to compute.