Question: I am a beginner in mlflow and was trying to set it up locally using Anaconda 3. I have created a new environment in anaconda and install mlflow and sklearn in it. Now I am using jupyter notebook to run my sample code for mlflow. ''' 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 logging.basicConfig(level=logging.WARN) logger = logging.getLogger(__name__) warnings.filterwarnings("ignore") np.random.seed(40) mlflow.set_tracking_uri("file:///Users/Swapnil/Documents/LocalPython/MLFLowDemo/mlrun") mlflow.get_tracking_uri() mlflow.get_experiment #experiment_id = mlflow.create_experiment("Mlflow_demo") experiment_id = mlflow.create_experiment("Demo3") experiment = mlflow.get_experiment(experiment_id) print("Name: {}".format(experiment.name)) print("Experiment_id: {}".format(experiment.experiment_id)) print("Artifact Location: {}".format(experiment.artifact_location)) print("Tags: {}".format(experiment.tags)) print("Lifecycle_stage: {}".format(experiment.lifecycle_stage)) mlflow.set_experiment("Demo3") 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 # Read the wine-quality csv file from the URL csv_url =\ 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv' try: data = pd.read_csv(csv_url, sep=';') except Exception as e: logger.exception( "Unable to download training & test CSV, check your internet connection. Error: %s", e) data.head(2) def train_model(data, alpha, l1_ratio): # 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"]] # Set default values if no alpha is provided alpha = alpha l1_ratio = l1_ratio # 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 with mlflow.start_run(experiment_id = experiment_id): 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) mlflow.sklearn.log_model(lr, "model") train_model(data, 0.5, 0.5) train_model(data, 0.5, 0.3) train_model(data, 0.4, 0.3)  ''' using above code, I am successfully able to create 3 different experiment as I can see the folders created in my local directory as shown below: enter image description here Now, I am trying to run the mlflow ui using the jupyter terminal in my chrome browser and I am able to open the mlflow ui but cannot see and experiments as shown below: enter image description here Could you help me in finding where I am going wrong?

Answer: Where do you run mlflow ui command? I think if you pass tracking ui path in the arguments, it would work: mlflow ui --backend-store-uri file:///Users/Swapnil/Documents/LocalPython/MLFLowDemo/mlrun