update in the retraining of trainingjob

Issue-Id: AIMLFW-65

Change-Id: I92e2b3087d12bbb843f1b6da43c3e7c0fb2257fb
Signed-off-by: rajdeep11 <rajdeep.sin@samsung.com>

diff --git a/tests/test_tm_apis.py b/tests/test_tm_apis.py
index 6a34031..77944b0 100644 (file)
--- a/tests/test_tm_apis.py
+++ b/tests/test_tm_apis.py
@@ -749,7 +749,7 @@ class Test_retraining:
         self.logger = trainingmgr_main.LOGGER
         
     #test_positive_1
-    db_result = [('mynetwork', 'testing', '*', 'testing_pipeline', 'Default', '{"arguments": {"epochs": "1", "trainingjob_name": "mynetwork"}}', '', datetime.datetime(2023, 2, 9, 9, 2, 11, 13916), 'No data available', '{"DATA_EXTRACTION": "FINISHED", "DATA_EXTRACTION_AND_TRAINING": "IN_PROGRESS", "TRAINING": "NOT_STARTED", "TRAINING_AND_TRAINED_MODEL": "NOT_STARTED", "TRAINED_MODEL": "NOT_STARTED"}', datetime.datetime(2023, 2, 9, 9, 2, 11, 13916), 1, False, '2', '{"datalake_source": {"InfluxSource": {}}}', 'No data available.', '', 'liveCell', 'UEData', False)]
+    db_result = [('mynetwork', 'testing', '*', 'testing_pipeline', 'Default', '{"arguments": {"epochs": "1", "trainingjob_name": "mynetwork"}}', '', datetime.datetime(2023, 2, 9, 9, 2, 11, 13916), 'No data available', '{"DATA_EXTRACTION": "FINISHED", "DATA_EXTRACTION_AND_TRAINING": "IN_PROGRESS", "TRAINING": "NOT_STARTED", "TRAINING_AND_TRAINED_MODEL": "NOT_STARTED", "TRAINED_MODEL": "NOT_STARTED"}', datetime.datetime(2023, 2, 9, 9, 2, 11, 13916), 1, False, '2', '{"datalake_source": {"InfluxSource": {}}}', 'No data available.', '', 'liveCell', 'UEData', False, False, '','')]
     mocked_TRAININGMGR_CONFIG_OBJ=mock.Mock(name="TRAININGMGR_CONFIG_OBJ")
     attrs_TRAININGMGR_CONFIG_OBJ = {'my_ip.return_value': '123'}
     mocked_TRAININGMGR_CONFIG_OBJ.configure_mock(**attrs_TRAININGMGR_CONFIG_OBJ)

diff --git a/trainingmgr/trainingmgr_main.py b/trainingmgr/trainingmgr_main.py
index dbfcdee..1d0bbd0 100644 (file)
--- a/trainingmgr/trainingmgr_main.py
+++ b/trainingmgr/trainingmgr_main.py
@@ -1066,6 +1066,9 @@ def retraining():
             datalake_source = get_one_key(json.loads(results[0][14])["datalake_source"])
             _measurement = results[0][17]
             bucket = results[0][18]
+            is_mme=results[0][20]
+            model_name=results[0][21]
+            model_info=results[0][22]
 
             notification_url = ""
             if "notification_url" in obj:
@@ -1075,11 +1078,11 @@ def retraining():
                 query_filter = obj['feature_filter']
 
             try:
-                add_update_trainingjob(description, pipeline_name, experiment_name,
-                                      featuregroup_name, arguments, query_filter, False,
-                                      enable_versioning, pipeline_version,
-                                      datalake_source, trainingjob_name, PS_DB_OBJ,
-                                      notification_url, _measurement, bucket)
+                add_update_trainingjob(description, pipeline_name, experiment_name, featuregroup_name,
+                                    arguments, query_filter, False, enable_versioning,
+                                    pipeline_version, datalake_source, trainingjob_name, 
+                                    PS_DB_OBJ, _measurement=_measurement,
+                                    bucket=bucket, is_mme=is_mme, model_name=model_name, model_info=model_info)
             except Exception as err:
                 not_possible_to_retrain.append(trainingjob_name)
                 LOGGER.debug(str(err) + "(training job is " + trainingjob_name + ")")