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Adding an MLFlow app

To add a new MLFlow app to your working instance, go to the Applications view and select the "MLFlow 2.0 + Jupyter" application type:
Start your application, MLFlow will open in a new JupyterLab editor tab:

Tracking model training

MLFlow server runs on port, you will need to set this tracking server in your code with:
import mlflow mlflow.set_tracking_uri("http://localhost:8080")
The following tutorial, adapted from the MLFlow documentation, shows how to track model training and register the trained model with MLFlow on Nuvolos:
# The data set used in this example is from
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
import os
import warnings
import sys
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from urllib.parse import urlparse
import mlflow
import mlflow.sklearn
import logging
logger = logging.getLogger(__name__)
def eval_metrics(actual, pred):
rmse = np.sqrt(mean_squared_error(actual, pred))
mae = mean_absolute_error(actual, pred)
r2 = r2_score(actual, pred)
return rmse, mae, r2
if __name__ == "__main__":
mlflow.set_experiment("Wine Quality")
# Read the wine-quality csv file from the URL
csv_url = (
data = pd.read_csv(csv_url, sep=";")
except Exception as e:
"Unable to download training & test CSV, check your internet connection. Error: %s", e
# Split the data into training and test sets. (0.75, 0.25) split.
train, test = train_test_split(data)
# The predicted column is "quality" which is a scalar from [3, 9]
train_x = train.drop(["quality"], axis=1)
test_x = test.drop(["quality"], axis=1)
train_y = train[["quality"]]
test_y = test[["quality"]]
alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
with mlflow.start_run():
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42), train_y)
predicted_qualities = lr.predict(test_x)
(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
print(" RMSE: %s" % rmse)
print(" MAE: %s" % mae)
print(" R2: %s" % r2)
mlflow.log_param("alpha", alpha)
mlflow.log_param("l1_ratio", l1_ratio)
mlflow.log_metric("rmse", rmse)
mlflow.log_metric("r2", r2)
mlflow.log_metric("mae", mae)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
# Model registry does not work with file store
if tracking_url_type_store != "file":
# Register the model
# There are other ways to use the Model Registry, which depends on the use case,
# please refer to the doc for more information:
mlflow.sklearn.log_model(lr, "model", registered_model_name="ElasticnetWineModel")
mlflow.sklearn.log_model(lr, "model")