def main()

in xgboost_script_mode_local_training_and_serving/xgboost_script_mode_local_training_and_serving.py [0:0]

• 43 lines of code
• 1 McCabe index (conditional complexity)

def main():
    print('Starting model training.')
    print('Note: if launching for the first time in local mode, container image download might take a few minutes to complete.')

    hyperparameters = {
        "max_depth": "5",
        "eta": "0.2",
        "gamma": "4",
        "min_child_weight": "6",
        "subsample": "0.7",
        "objective": "reg:squarederror",
        "num_round": "50",
        "verbosity": "2",
    }

    xgb_script_mode_estimator = XGBoost(
        entry_point="./code/abalone.py",
        hyperparameters=hyperparameters,
        role=DUMMY_IAM_ROLE,
        instance_count=1,
        instance_type='local',
        framework_version="1.2-1"
    )

    train_input = TrainingInput("file://./data/train/abalone", content_type="text/libsvm")

    xgb_script_mode_estimator.fit({"train": train_input, "validation": train_input})

    print('Completed model training')

    model_data = xgb_script_mode_estimator.model_data
    print(model_data)

    xgb_inference_model = XGBoostModel(
        model_data=model_data,
        role=DUMMY_IAM_ROLE,
        entry_point="./code/inference.py",
        framework_version="1.2-1",
    )

    print('Deploying endpoint in local mode')
    predictor = xgb_inference_model.deploy(
        initial_instance_count=1,
        instance_type="local",
    )

    a_young_abalone = "6 1:3 2:0.37 3:0.29 4:0.095 5:0.249 6:0.1045 7:0.058 8:0.067"
    do_inference_on_local_endpoint(predictor, a_young_abalone)

    an_old_abalone = "15 1:1 2:0.655 3:0.53 4:0.175 5:1.2635 6:0.486 7:0.2635 8:0.415"
    do_inference_on_local_endpoint(predictor, an_old_abalone)

    print('About to delete the endpoint to stop paying (if in cloud mode).')
    predictor.delete_endpoint(predictor.endpoint_name)