port config

demo.py

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+import mlflow
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+import os
+
+# Load Iris dataset
+iris = load_iris()
+
+# Split dataset into X features and Target variable
+X = pd.DataFrame(data = iris["data"], columns= iris["feature_names"])
+y = pd.Series(data = iris["target"], name="target")
+
+# Split our training set and our test set 
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+# Visualize dataset 
+X_train.head()
+
+os.environ["APP_URI"] = "https://licorne2lc-mlflow.hf.space" # For demo purpose, teachers can use "https://antoinekrajnc-mlflow-server-demo.hf.space"
+
+# Set your variables for your environment
+EXPERIMENT_NAME="demo-mlflow-experiemnt"
+
+# Set tracking URI to your Hugging Face application
+mlflow.set_tracking_uri(os.environ["APP_URI"])
+
+# Set experiment's info 
+mlflow.set_experiment(EXPERIMENT_NAME)
+
+# Get our experiment info
+experiment = mlflow.get_experiment_by_name(EXPERIMENT_NAME)
+
+# Call mlflow autolog
+mlflow.sklearn.autolog()
+
+with mlflow.start_run(experiment_id = experiment.experiment_id):
+    # Specified Parameters 
+    c = 0.5
+
+    # Instanciate and fit the model 
+    lr = LogisticRegression(C=c)
+    lr.fit(X_train.values, y_train.values)
+
+    # Store metrics 
+    predicted_qualities = lr.predict(X_test.values)
+    accuracy = lr.score(X_test.values, y_test.values)
+
+    # Print results 
+    print("LogisticRegression model")
+    print("Accuracy: {}".format(accuracy))
+
+    # Log Metric 
+    mlflow.log_metric("Accuracy", accuracy)
+
+    # Log Param
+    mlflow.log_param("C", c)
+
+    # Log model 
+    mlflow.sklearn.log_model(lr, "model")