upload trained model to mme

Issue-Id: AIMLFW-65

Change-Id: Ib0acf11be847e8f2b2b9ac5897f74511fad40f5e
Signed-off-by: rajdeep11 <rajdeep.sin@samsung.com>

diff --git a/tests/test_tm_apis.py b/tests/test_tm_apis.py
index 77944b0..ed7733c 100644 (file)
--- a/tests/test_tm_apis.py
+++ b/tests/test_tm_apis.py
@@ -139,13 +139,18 @@ class Test_pipeline_notification:
     message1="Pipeline notification success."
     code1=status.HTTP_200_OK
     response_tuple1=({"result": message1}, code1)
+    db_result = [('usecase1', 'uc1', '*', 'qoe Pipeline lat v2', 'Default', '{"arguments": {"epochs": "1", "trainingjob_name": "usecase1"}}',
+     '', datetime.datetime(2022, 10, 12, 10, 0, 59, 923588), '51948a12-aee9-42e5-93a0-b8f4a15bca33',
+      '{"DATA_EXTRACTION": "FINISHED", "DATA_EXTRACTION_AND_TRAINING": "FINISHED", "TRAINING": "FINISHED", "TRAINING_AND_TRAINED_MODEL": "FINISHED", "TRAINED_MODEL": "FAILED"}',
+       datetime.datetime(2022, 10, 12, 10, 2, 31, 888830), 1, False, '3', '{"datalake_source": {"InfluxSource": {}}}', 'No data available.', '', 'liveCell', 'UEData', False, False, "","")]
     @patch('trainingmgr.trainingmgr_main.MM_SDK', return_value = mocked_mm_sdk)
     @patch('trainingmgr.trainingmgr_main.TRAININGMGR_CONFIG_OBJ', return_value = mocked_TRAININGMGR_CONFIG_OBJ) 
     @patch('trainingmgr.trainingmgr_main.change_steps_state_of_latest_version')
     @patch('trainingmgr.trainingmgr_main.update_model_download_url')
+    @patch('trainingmgr.trainingmgr_main.get_trainingjob_info_by_name', return_value=db_result)
     @patch('trainingmgr.trainingmgr_main.get_latest_version_trainingjob_name', return_value = "usecase1")
     @patch('trainingmgr.trainingmgr_main.response_for_training', return_value = response_tuple1)
-    def test_pipeline_notification(self,mock1, mock2, mock3, mock4, mock5, mock6):
+    def test_pipeline_notification(self,mock1, mock2, mock3, mock4, mock5, mock6, mock7):
         trainingmgr_main.LOGGER.debug("******* test_pipeline_notification post *******")
         trainingjob_req = {
                     "trainingjob_name":"usecase1",

diff --git a/trainingmgr/trainingmgr_main.py b/trainingmgr/trainingmgr_main.py
index 1d0bbd0..2668c3a 100644 (file)
--- a/trainingmgr/trainingmgr_main.py
+++ b/trainingmgr/trainingmgr_main.py
@@ -536,6 +536,22 @@ def pipeline_notification():
                 change_steps_state_of_latest_version(trainingjob_name, PS_DB_OBJ,
                                                         Steps.TRAINED_MODEL.name,
                                                         States.FINISHED.name)
+                # upload to the mme
+                trainingjob_info=get_trainingjob_info_by_name(trainingjob_name, PS_DB_OBJ)
+
+                is_mme= trainingjob_info[0][20]
+                if is_mme:   
+                    model_name=trainingjob_info[0][21] #model_name
+                    file=MM_SDK.get_model_zip(trainingjob_name, str(version))
+                    url ="http://"+str(TRAININGMGR_CONFIG_OBJ.model_management_service_ip)+":"+str(TRAININGMGR_CONFIG_OBJ.model_management_service_port)+"/uploadModel/{}".format(model_name)
+                    LOGGER.debug("url for upload is: ", url)
+                    resp2=requests.post(url=url, files={"file":('Model.zip', file, 'application/zip')})
+                    if resp2.status_code != status.HTTP_200_OK :
+                        errMsg= "Upload to mme failed"
+                        LOGGER.error(errMsg + trainingjob_name)
+                        raise TMException(errMsg + trainingjob_name)
+                    
+                    LOGGER.debug("Model uploaded to the MME")
             else:
                 errMsg = "Trained model is not available  "
                 LOGGER.error(errMsg + trainingjob_name)