我使用下面的函数（来自pydataberlin）使用MLflow和记录的参数。

def train(alpha=0.5,l1_ratio=0.5):
    # train a model with given parameters
    warnings.filterwarnings("ignore")
    np.random.seed(40)

    # Read the wine-quality csv file (make sure you're running this from the root of MLflow!)
    data_path = "data/wine-quality.csv"
    train_x,train_y,test_x,test_y = load_data(data_path)

    # Useful for multiple runs (only doing one run in this sample notebook)    
    with mlflow.start_run():
        # Execute ElasticNet
        lr = ElasticNet(alpha=alpha,l1_ratio=l1_ratio,random_state=42)
        lr.fit(train_x,train_y)

        # Evaluate Metrics
        predicted_qualities = lr.predict(test_x)
        (rmse,mae,r2) = eval_metrics(test_y,predicted_qualities)

        # Print out metrics
        print("Elasticnet model (alpha=%f,l1_ratio=%f):" % (alpha,l1_ratio))
        print("  RMSE: %s" % rmse)
        print("  MAE: %s" % mae)
        print("  R2: %s" % r2)

        # Log parameter,metrics,and model to MLflow
        mlflow.log_param(key="alpha",value=alpha)
        mlflow.log_param(key="l1_ratio",value=l1_ratio)
        mlflow.log_metric(key="rmse",value=rmse)
        mlflow.log_metrics({"mae": mae,"r2": r2})
        mlflow.log_artifact(data_path)
        print("Save to: {}".format(mlflow.get_artifact_uri()))

        mlflow.sklearn.log_model(lr,"model")

一旦使用其参数运行trin（），在UI中将看不到工件，但可以看到模型及其参数和度量。

在工件选项卡中，其写为​​No Artifacts Recorded Use the log artifact APIs to store file outputs from MLflow runs.，但是在模型文件夹的查找器中，所有工件都与模型Pickle存在。

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