Update example DAG for AI Platform operators (#9727)

airflow/providers/google/cloud/example_dags/example_mlengine.py

@@ -25,9 +25,10 @@
 from airflow import models
 from airflow.operators.bash import BashOperator
 from airflow.providers.google.cloud.operators.mlengine import (
-    MLEngineCreateVersionOperator, MLEngineDeleteModelOperator, MLEngineDeleteVersionOperator,
-    MLEngineListVersionsOperator, MLEngineManageModelOperator, MLEngineSetDefaultVersionOperator,
-    MLEngineStartBatchPredictionJobOperator, MLEngineStartTrainingJobOperator,
+    MLEngineCreateModelOperator, MLEngineCreateVersionOperator, MLEngineDeleteModelOperator,
+    MLEngineDeleteVersionOperator, MLEngineGetModelOperator, MLEngineListVersionsOperator,
+    MLEngineSetDefaultVersionOperator, MLEngineStartBatchPredictionJobOperator,
+    MLEngineStartTrainingJobOperator,
 )
 from airflow.providers.google.cloud.utils import mlengine_operator_utils
 from airflow.utils.dates import days_ago
@@ -66,30 +67,26 @@
         project_id=PROJECT_ID,
         region="us-central1",
         job_id="training-job-{{ ts_nodash }}-{{ params.model_name }}",
-        runtime_version="1.14",
-        python_version="3.5",
+        runtime_version="1.15",
+        python_version="3.7",
         job_dir=JOB_DIR,
         package_uris=[TRAINER_URI],
         training_python_module=TRAINER_PY_MODULE,
         training_args=[],
     )
 
-    create_model = MLEngineManageModelOperator(
+    create_model = MLEngineCreateModelOperator(
         task_id="create-model",
         project_id=PROJECT_ID,
-        operation='create',
         model={
             "name": MODEL_NAME,
         },
     )
 
-    get_model = MLEngineManageModelOperator(
+    get_model = MLEngineGetModelOperator(
         task_id="get-model",
         project_id=PROJECT_ID,
-        operation="get",
-        model={
-            "name": MODEL_NAME,
-        }
+        model_name=MODEL_NAME,
     )
 
     get_model_result = BashOperator(
@@ -105,10 +102,10 @@
             "name": "v1",
             "description": "First-version",
             "deployment_uri": '{}/keras_export/'.format(JOB_DIR),
-            "runtime_version": "1.14",
+            "runtime_version": "1.15",
             "machineType": "mls1-c1-m2",
             "framework": "TENSORFLOW",
-            "pythonVersion": "3.5"
+            "pythonVersion": "3.7"
         }
     )
 
@@ -120,10 +117,10 @@
             "name": "v2",
             "description": "Second version",
             "deployment_uri": SAVED_MODEL_PATH,
-            "runtime_version": "1.14",
+            "runtime_version": "1.15",
             "machineType": "mls1-c1-m2",
             "framework": "TENSORFLOW",
-            "pythonVersion": "3.5"
+            "pythonVersion": "3.7"
         }
     )
 
@@ -148,7 +145,7 @@
     prediction = MLEngineStartBatchPredictionJobOperator(
         task_id="prediction",
         project_id=PROJECT_ID,
-        job_id="prediciton-{{ ts_nodash }}-{{ params.model_name }}",
+        job_id="prediction-{{ ts_nodash }}-{{ params.model_name }}",
         region="us-central1",
         model_name=MODEL_NAME,
         data_format="TEXT",
@@ -203,13 +200,13 @@ def validate_err_and_count(summary: Dict) -> Dict:
         return summary
 
     evaluate_prediction, evaluate_summary, evaluate_validation = mlengine_operator_utils.create_evaluate_ops(
-        task_prefix="evalueate-ops",  # pylint: disable=too-many-arguments
+        task_prefix="evaluate-ops",
         data_format="TEXT",
         input_paths=[PREDICTION_INPUT],
         prediction_path=PREDICTION_OUTPUT,
         metric_fn_and_keys=get_metric_fn_and_keys(),
         validate_fn=validate_err_and_count,
-        batch_prediction_job_id="evalueate-ops-{{ ts_nodash }}-{{ params.model_name }}",
+        batch_prediction_job_id="evaluate-ops-{{ ts_nodash }}-{{ params.model_name }}",
         project_id=PROJECT_ID,
         region="us-central1",
         dataflow_options={