Change run or experiment dynamically

[cpinon-grd]

Hello, I have set a dynamic modular pipeline that runs model training for different models:

def create_pipeline() -> Pipeline:
    """This function will create a complete modelling
    pipeline that consolidates a single shared 'split' stage,
    several modular instances of the 'train test evaluate' stage
    and returns a single, appropriately namespaced Kedro pipeline
    object.
    """

    pipes = []
    log.debug(
        f"settings.DYNAMIC_PIPELINES_MAPPING.items(): {settings.DYNAMIC_PIPELINES_MAPPING.items()}"
    )

    for namespace, variants in settings.DYNAMIC_PIPELINES_MAPPING.items():
        log.debug(f"namespace: {namespace}")
        log.debug(f"variants: {variants}")

        for variant in variants:
            pipes.append(
                pipeline(
                    pipe=new_train_template(),
                    inputs={
                        "input_table": "input_table",
                    },
                    parameters={
                        "model_options": f"{namespace}.{variant}.model_options",
                    },
                    namespace=f"{namespace}.{variant}",
                    tags=[variant, namespace],
                )
            )
    return sum(pipes)

The problem is that mlflow considers that all these models belong to the same mlflow run, so i get this error:

[08/20/24 16:31:29] ERROR    Error during training: Changing param values is not allowed. Param with key='threshold' was already logged with value='{'threshold_q90':        nodes.py:125
                             0.5356575641792262, 'threshold_q95': 0.612233234673521, 'threshold_q99': 0.7159441011394121}' for run ID='d3bb8837e8dc4cf28389816f67841d53'.                
                             Attempted logging new value '{'threshold_q90': 0.5369235873602624, 'threshold_q95': 0.6154837134825515, 'threshold_q99': 0.6978202366080968}'.              
                                                                                                                                                                                         
                             The cause of this error is typically due to repeated calls                                                                                                  
                             to an individual run_id event logging.   

Is it possible to change run name or even experiment dynamically for the different model training pipelines?

Thank you!