But this works strangely:
` @PipelineDecorator.component(cache=False, execution_queue="default")
def get_param():
return 'hello'

@PipelineDecorator.component(cache=False, execution_queue="default")
def get_best_model(task_ids):
import ...

print('task_ids:', task_ids, type(task_ids)) # task_ids: None <class 'NoneType'>

...

@PipelineDecorator.pipeline(
name='...',
project='...',
version='0.1'
)
def pipeline_entry(task_ids: List[str], ...):
print(task_ids, type(task_ids)) # ['a8f9a023a7404400a27aff44a945cff1', 'c63f6606ef76475cb9549ad71aaeaff6', 'a40d6eca53a347dd9b383afa65560960'] <class 'list'>
param = get_param()
get_best_model(param)
...

if name == 'main':
PipelineDecorator.run_locally()

pipeline_entry(
    task_ids=[
        'a8f9a023a7404400a27aff44a945cff1',
        'c63f6606ef76475cb9549ad71aaeaff6',
        'a40d6eca53a347dd9b383afa65560960',
    ]
) `

Yes sure CostlyOstrich36 , I'm just trying to pass some arguments from my __main__ to my pipeline_entry() to my component get_best_model() . But for some reason, I'm getting None into get_best_model instead of what I've given it in pipeline_entry

Hmm let me check I think you are correct here

1.7.1

Okay, thanks

Hmm there was a commit there that was "fixing" some stuff , apparently it also broke it
Let me see if we can push an RC (I know a few things are already waiting to be released)

SmugSnake6 what's the clearml version you are using ?

Weeell it seems to work with version 1.7.0 and not with 1.7.1

Hi SmugSnake6 , can you please elaborate on what exactly is happening and what you were expecting to happen?

This will fix it, the issue is the "no default value" that breaks the casting
@PipelineDecorator.component(cache=False) def step_one(my_arg=""):

CostlyOstrich36 This looks like a bug? Here's a simpler version of it and what I'm getting:
` from clearml.automation.controller import PipelineDecorator

@PipelineDecorator.component(cache=False)
def step_one(my_arg):
print('step_one/my_arg:', my_arg) # step_one/my_arg: None
# I should not get None here! At least that's what I'm expecting

@PipelineDecorator.pipeline(name='custom pipeline logic', project='examples', version='0.0.5')
def executing_pipeline(my_arg):
print('my_arg:', my_arg) # my_arg: hello
step_one(my_arg)

if name == 'main':
PipelineDecorator.run_locally()

executing_pipeline(
    my_arg='hello',
) `