From: rajdeep11 Date: Thu, 21 Nov 2024 06:22:28 +0000 (+0530) Subject: removed the is_mme in the code X-Git-Tag: 3.0.0~33^2 X-Git-Url: https://gerrit.o-ran-sc.org/r/gitweb?a=commitdiff_plain;h=861b17a7a29c9ffbd10662e0cbbb6a627397d4c3;p=aiml-fw%2Fawmf%2Ftm.git removed the is_mme in the code Change-Id: I657911875c7904dfa79a84f80d6f23778d77c812 Signed-off-by: rajdeep11 --- diff --git a/tests/test_tm_apis.py b/tests/test_tm_apis.py index 320f7f0..8aa4d71 100644 --- a/tests/test_tm_apis.py +++ b/tests/test_tm_apis.py @@ -282,7 +282,6 @@ class Test_get_trainingjob_by_name_version: creation_time = datetime.datetime.now() updation_time = datetime.datetime.now() training_config = { - "is_mme" : True, "description": "Test description", "dataPipeline": { "feature_group_name": "test_feature_group", @@ -309,9 +308,7 @@ class Test_get_trainingjob_by_name_version: version=1, model_url="http://test.model.url", notification_url="http://test.notification.url", - deletion_in_progress=False, - model_name="test_model", - model_info="test_model_info" + deletion_in_progress=False ) @pytest.fixture @@ -341,9 +338,6 @@ class Test_get_trainingjob_by_name_version: assert job_data['training_config']['description'] == "Test description" assert job_data['training_config']['dataPipeline']['feature_group_name'] == "test_feature_group" assert job_data['training_config']['trainingPipeline']['pipeline_name'] == "test_pipeline" - assert job_data['training_config']['is_mme'] is True - assert job_data['model_name'] == "test_model" - assert job_data['model_info'] == "test_model_info" assert job_data['accuracy'] == mock_metrics @patch('trainingmgr.trainingmgr_main.check_trainingjob_name_and_version', return_value=False) diff --git a/tests/test_trainingmgr_util.py b/tests/test_trainingmgr_util.py index 85002c6..aa404bc 100644 --- a/tests/test_trainingmgr_util.py +++ b/tests/test_trainingmgr_util.py @@ -272,9 +272,9 @@ class Test_check_trainingjob_data: @patch('trainingmgr.common.trainingmgr_util.isinstance',return_value=True) def test_check_trainingjob_data(self,mock1,mock2): usecase_name = "usecase8" - json_data = { "description":"unittest", "featureGroup_name": "group1" , "pipeline_name":"qoe" , "experiment_name":"experiment1" , "arguments":"arguments1" , "query_filter":"query1" , "enable_versioning":True , "target_deployment":"Near RT RIC" , "pipeline_version":1 , "datalake_source":"cassandra db" , "incremental_training":True , "model":"usecase7" , "model_version":1 , "is_mme":False, "model_name":""} + json_data = { "description":"unittest", "featureGroup_name": "group1" , "pipeline_name":"qoe" , "experiment_name":"experiment1" , "arguments":"arguments1" , "query_filter":"query1" , "enable_versioning":True , "target_deployment":"Near RT RIC" , "pipeline_version":1 , "datalake_source":"cassandra db" , "incremental_training":True , "model":"usecase7" , "model_version":1 } - expected_data = ("group1", 'unittest', 'qoe', 'experiment1', 'arguments1', 'query1', True, 1, 'cassandra db',False, "") + expected_data = ("group1", 'unittest', 'qoe', 'experiment1', 'arguments1', 'query1', True, 1, 'cassandra db') assert check_trainingjob_data(usecase_name, json_data) == expected_data,"data not equal" def test_negative_check_trainingjob_data_1(self): diff --git a/trainingmgr/common/trainingConfig_parser.py b/trainingmgr/common/trainingConfig_parser.py index 9467c67..b3155fc 100644 --- a/trainingmgr/common/trainingConfig_parser.py +++ b/trainingmgr/common/trainingConfig_parser.py @@ -40,7 +40,6 @@ def __getLeafPaths(): It returns paths possible to retrieve data Based on TrainingConfig Schema: { - "is_mme" : false, "description": "This is something3", "dataPipeline": { "feature_group_name": "base2", @@ -54,7 +53,6 @@ def __getLeafPaths(): } ''' paths = { - "is_mme": ["is_mme"], "description": ["description"], "feature_group_name": ["dataPipeline", "feature_group_name"], "query_filter" : ["dataPipeline", "query_filter"], diff --git a/trainingmgr/common/trainingmgr_util.py b/trainingmgr/common/trainingmgr_util.py index f63854f..083ccd7 100644 --- a/trainingmgr/common/trainingmgr_util.py +++ b/trainingmgr/common/trainingmgr_util.py @@ -139,8 +139,7 @@ def check_trainingjob_data(trainingjob_name, json_data): "pipeline_name", "experiment_name", "arguments", "enable_versioning", "datalake_source", "description", - "query_filter", - "is_mme", "model_name"], json_data): + "query_filter"], json_data): description = json_data["description"] feature_list = json_data["featureGroup_name"] @@ -154,8 +153,6 @@ def check_trainingjob_data(trainingjob_name, json_data): enable_versioning = json_data["enable_versioning"] pipeline_version = json_data["pipeline_version"] datalake_source = json_data["datalake_source"] - is_mme=json_data["is_mme"] - model_name=json_data["model_name"] else : raise TMException("check_trainingjob_data- supplied data doesn't have" + \ "all the required fields ") @@ -164,7 +161,7 @@ def check_trainingjob_data(trainingjob_name, json_data): str(err)) from None return (feature_list, description, pipeline_name, experiment_name, arguments, query_filter, enable_versioning, pipeline_version, - datalake_source, is_mme, model_name) + datalake_source) def check_feature_group_data(json_data): """ diff --git a/trainingmgr/models/trainingjob.py b/trainingmgr/models/trainingjob.py index d12e6fe..4c6eb33 100644 --- a/trainingmgr/models/trainingjob.py +++ b/trainingmgr/models/trainingjob.py @@ -52,9 +52,7 @@ class TrainingJob(db.Model): training_config = Column(String(5000), nullable=False) model_url = Column(String(1000), nullable=True) notification_url = Column(String(1000), nullable=True) - model_name = db.Column(db.String(128), nullable=True) model_id = Column(Integer, nullable=False) - model_info = Column(String(1000), nullable=True) #defineing relationships steps_state = relationship("TrainingJobStatus", back_populates="trainingjobs") diff --git a/trainingmgr/trainingmgr_main.py b/trainingmgr/trainingmgr_main.py index 8251f4a..dbf2e4c 100644 --- a/trainingmgr/trainingmgr_main.py +++ b/trainingmgr/trainingmgr_main.py @@ -146,8 +146,6 @@ def get_trainingjob_by_name_version(trainingjob_name, version): url for downloading model notification_url: str url of notification server - is_mme: boolean - whether the mme is enabled model_name: str model name model_info: str @@ -191,9 +189,6 @@ def get_trainingjob_by_name_version(trainingjob_name, version): # "datalake_source": get_one_key(json.loads(trainingjob.datalake_source)['datalake_source']), "model_url": trainingjob.model_url, "notification_url": trainingjob.notification_url, - # "is_mme": trainingjob.is_mme, - "model_name": trainingjob.model_name, - "model_info": trainingjob.model_info, "accuracy": data } response_data = {"trainingjob": dict_data} @@ -605,19 +600,6 @@ def pipeline_notification(): States.FINISHED.name) notification_rapp(trainingjob_info, TRAININGMGR_CONFIG_OBJ) # upload to the mme - is_mme = getField(trainingjob_info.training_config, "is_mme") - if is_mme: - model_name=trainingjob_info.model_name #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) @@ -945,8 +927,6 @@ def trainingjob_operations(trainingjob_name): Name of model trainingConfig: dict Training-Configurations, parameter as follows - is_mme: boolean - whether mme is enabled description: str description dataPipeline: dict @@ -1006,26 +986,6 @@ def trainingjob_operations(trainingjob_name): processed_json_data = request.get_json() processed_json_data['training_config'] = json.dumps(request.get_json()["training_config"]) trainingjob = trainingjob_schema.load(processed_json_data) - model_info="" - if getField(trainingjob.training_config, "is_mme"): - pipeline_dict =json.loads(TRAININGMGR_CONFIG_OBJ.pipeline) - model_info=get_model_info(TRAININGMGR_CONFIG_OBJ, trainingjob.model_name) - s=model_info["meta-info"]["feature-list"] - model_type=model_info["meta-info"]["model-type"] - try: - pipeline_name=pipeline_dict[str(model_type)] - except Exception as err: - err="Doesn't support the model type" - raise TMException(err) - pipeline_version=pipeline_name - feature_list=','.join(s) - result= get_feature_groups_db(PS_DB_OBJ) - for res in result: - if feature_list==res[1]: - featuregroup_name=res[0] - break - if featuregroup_name =="": - return {"Exception":"The no feature group with mentioned feature list, create a feature group"}, status.HTTP_406_NOT_ACCEPTABLE add_update_trainingjob(trainingjob, True) api_response = {"result": "Information stored in database."} response_code = status.HTTP_201_CREATED