Hi,
I am creating pipeline from function with dynamically created steps, eg. if I pass pipeline param tune_optime='recall,precision', my pipeline is creating 2 tasks/steps - each for trained model. Everything is working really nice, when I start pipeline from script. Pipeline is loaded and send to agents. In GUI also looks fine.
The problem is, when I start "NEW RUN" from GUI and change value of my key parameters.
When I add new value to tune_optimize, I see that it was loaded by pipeline (printing pipe._task.get_parameters_as_dict()['Args']), but new step was not created.
When I reduce tune_optime value to just 'recall'. Pipeline execution failed with msg:
ValueError: Node 'tune_et_for_Precision', base_task_id is empty .
Should it work like that by design?
Why it is working fine when I execute pipeline from script, but not when started in GUI?

My code:
` from clearml import PipelineController

def step_preprocessing():
print('preprocessing time!')
return

def step_training():
print('training time!')
return

def step_tune(model_task_id):
print('tune time!')
return

if name == 'main':

pipe = PipelineController(
    name='TestFunction',
    project='Test',
    version='1.0.0',
    add_pipeline_tags=False,
)

# Dataset parameters
pipe.add_parameter(name='models_list', description='model_list', default='et,dt')
pipe.add_parameter(name='tune_optimize', description='optimize', default="Recall,Precision")

print('pipe._task.get_parameters_as_dict():')
print(pipe._task.get_parameters_as_dict())

print('pipe._pipeline_args:')
print(pipe._pipeline_args)

# During pipeline initialisation pipeline_params is empty and we need to use default values.
# When pipeline start the run, params are lunched again, and then pipeline_params can be used.
try:
    pipeline_params = pipe._task.get_parameters_as_dict()['Args']
except KeyError:
    pipeline_params = pipe._pipeline_args

print(f'Pipeline params:\n{pipeline_params}')
pipe.add_function_step(
    name='preprocessing',
    function=step_preprocessing,
    function_return=['data_frame'],
    cache_executed_step=True,
)

models_name = pipeline_params['models_list'].replace(" ", "").split(',')
optimizers = pipeline_params['tune_optimize'].replace(" ", "").split(',')

for model_name in models_name:
    tune_function_kwargs = {}
    pipe.add_function_step(
        name=f'{model_name}_training',
        project_name='Test/Research',
        task_name=f"train_{model_name}",
        task_type='training',
        parents=['preprocessing'],
        function=step_training,
        function_return=['out'],
        cache_executed_step=False,
    )
    tune_function_kwargs["model_task_id"] = f'${{{model_name}_training.id}}'
    counter=1
    for item in optimizers:
        pipe.add_function_step(
            name=f'tune_{model_name}_for_{item}',
            project_name='Test/Research',
            task_name=f"tune_{model_name}_{item}",
            task_type='optimizer',
            function=step_tune,
            function_kwargs=tune_function_kwargs,
            function_return=['out'],
            cache_executed_step=False,
        )
        counter = counter + 1

pipe.start(queue='TestQUEUE')
print('process completed') `