AgitatedDove14 I really don't know how is this possible... I tried upgrading the server, tried whatever I could

About small toy code to reproduce I just don't have the time for that, but I will paste the callback I am using to this explanation. This is the overall logic so you can replicate and use my callback

From the pipeline task, launch some sub tasks, and put in their post_execute_callback the .collect_description_tables method from my callback class (attached below) Run the pipeline locally, e.g. pipe.start_locally(run_pipeline_steps_locally=True) After (2) is done, call .process_results()
my callback:

` class MedianPredictionCollector:

_tasks_to_collect = list()
_apps = list()
_medians = list()
_pipeline_task = clearml.Task.current_task()

@classmethod
def collect_description_tables(cls, pipeline: clearml.PipelineController, node: clearml.PipelineController.Node):
    # Collect tasks
    cls._tasks_to_collect.append(node.executed)

@classmethod
def process_results(cls):
    """
    Summarize all median predictions into one table and attach as artifact to the pipeline task

    :return: None
    """

    # Collect median predictions
    for task_id in cls._tasks_to_collect:
        current_task = clearml.Task.get_task(task_id)
        median_prediction = current_task.artifacts['inference_description_table'].get().loc[5]
        app = clearml.Task.get_task(task_id=current_task.get_parameter('Args/task_id')).get_parameter(
            'Args/application')

        cls._apps.append(app)
        cls._medians.append(median_prediction)

    # Summary table
    median_predictions = pd.DataFrame(index=cls._apps, data=cls._medians)

    # Upload to pipeline
    cls._pipeline_task.upload_artifact('Median Predictions', median_predictions)
    # I also tried not swapping this line with clearml.Task.current_task().upload_artifact ... didn't work `