def lambda_handler()

in assets/functions/ml_pipeline/load_pipeline_parameter/app.py [0:0]


def lambda_handler(event, context):

    if 'train_model' in event:
        train_model = event['train_model']
    else:
        train_model = False

    if train_model:
        unique_id = str(uuid.uuid4())

        model_name = "model-{}".format(unique_id)
        job_name = "training-job-{}".format(unique_id)

        write_config("ModelName", model_name)
        write_config("Training_job_name", job_name)

    else:
        model_name = get_config("ModelName")
        job_name = get_config("Training_job_name")

    parameter = {
        "Data_start": get_config("Data_start"),
        "Data_end": get_config("Data_end"),
        "Forecast_period": int(get_config("Forecast_period")),
        "Training_samples": int(get_config("Training_samples")),
        "Training_instance_type": get_config("Training_instance_type"),
        "Endpoint_instance_type": get_config("Endpoint_instance_type"),
        "Meter_start": int(get_config("Meter_start")),
        "Meter_end": int(get_config("Meter_end")),
        "Batch_size": int(get_config("Batch_size")),
        "ML_endpoint_name": get_config("ML_endpoint_name"),
        "ModelName": model_name,
        "Training_job_name": job_name
    }

    return {
        **parameter,
        **event
    }