We get a little further on the azml_02 but it seems to fail still

azuremlpythonsdk-v2/diabetes_hyperdrive/diabetes_training.py

@@ -17,28 +17,28 @@ def main():
     # Input and output arguments
 
     # Get script arguments
-    parser = XXXX()
+    parser = argparse()
 
     # Input dataset
     parser.add_argument(
-        "XXXX",
+        "--data",
         type=str,
         help="path to input data",
     )
 
     # Model name
-    parser.add_argument("XXXX", type=str, help="model name")
+    parser.add_argument("--registered_model_name", type=str, help="model name")
 
     # Hyperparameters
     parser.add_argument(
-        "XXXX",
+        "--learning_rate",
         type=float,
         dest="learning_rate",
         default=0.1,
         help="learning rate",
     )
     parser.add_argument(
-        "XXXX",
+        "--n_estimators",
         type=int,
         dest="n_estimators",
         default=100,
@@ -50,10 +50,10 @@ def main():
     print(" ".join(f"{k}={v}" for k, v in vars(args).items()))
 
     # Start Logging
-    mlflow.XXXX()
+    mlflow.start_run()
 
     # enable autologging
-    mlflow.XXXX()
+    mlflow.sklearn.autolog()
 
     # load the diabetes data (passed as an input dataset)
     print("input data:", args.data)
@@ -78,32 +78,32 @@ def main():
     )
 
     # Split data into training set and test set
-    X_train, X_test, y_train, y_test = XXXX(
+    X_train, X_test, y_train, y_test = train_test_split(
         X, y, test_size=0.30, random_state=0
     )
 
     # Train a Gradient Boosting classification model
     # with the specified hyperparameters
     print("Training a classification model")
-    model = XXXX(
-        learning_rate=XXXX, n_estimators=XXXX
+    model = GradientBoostingClassifier(
+        learning_rate=args.learning_rate, n_estimators=args.n_estimators
     ).fit(X_train, y_train)
 
     # calculate accuracy
-    y_hat = model.XXXX(X_test)
+    y_hat = model.predict(X_test)
     accuracy = np.average(y_hat == y_test)
     print("Accuracy:", accuracy)
     mlflow.log_metric("Accuracy", float(accuracy))
 
     # calculate AUC
-    y_scores = model.XXXX(X_test)
+    y_scores = model.predict_proba(X_test)
     auc = roc_auc_score(y_test, y_scores[:, 1])
     print("AUC: " + str(auc))
     mlflow.log_metric("AUC", float(auc))
 
     # Registering the model to the workspace
     print("Registering the model via MLFlow")
-    mlflow.XXXX(
+    mlflow.sklearn.log_model(
         sk_model=model,
         registered_model_name=args.registered_model_name,
         artifact_path=args.registered_model_name,
@@ -116,7 +116,7 @@ def main():
     )
 
     # Stop Logging
-    mlflow.XXXX()
+    mlflow.end_run()
 
 
 if __name__ == "__main__":