This function shows the same behaviour once the task gets initialized:

# Training helper functions
def prepare_training(env: dict, model_variant: str, dataset_id: str, args: dict, project: str = "LVGL UI Detector"):
    from clearml import Task, Dataset
    import os
    print(f"Training {model_variant} on dataset: {dataset_id}")
    # Fetch dataset YAML
    env['FILES'][dataset_id] = Dataset.get(dataset_id).list_files("*.yaml")
    # Download & modify dataset
    env['DIRS']['target'] = download_dataset(env, dataset_id)
    dataset_file = os.path.join(env['DIRS']['target'], env['FILES'][dataset_id][0])
    dataset_content = fix_dataset_path(dataset_file, env['DIRS']['target'])
    args['data'] = os.path.join(env['DIRS']['target'], env['FILES'][dataset_id][0])
    # Create a ClearML Task
    task = Task.init(
        project_name="LVGL UI Detector",
        task_name=f"Train {model_variant} ({env['DATASETS'][dataset_id]['name']})",
        task_type=Task.TaskTypes.training
    )
    task.connect(args)
    # Log "model_variant" parameter to task
    task.set_parameter("model_variant", model_variant)
    task.set_parameter("dataset", dataset_id)
    task.set_parameter("General/data", args['data'])
    task.connect_configuration(name="Dataset YAML", configuration=args['data'])
    task.connect_configuration(name="Dataset Content", configuration=dataset_content)
    return task.id