# See the License for the specific language governing permissions and
# limitations under the License.
#
-# ==================================================================================
\ No newline at end of file
+# ==================================================================================
+
+from .trainingjob_controller import training_job_controller
+from .featuregroup_controller import featuregroup_controller
+
+__all__ = ['training_job_controller', 'featuregroup_controller']
\ No newline at end of file
--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# ==================================================================================
+import json
+from flask_api import status
+from flask import Blueprint, jsonify, request
+from marshmallow import ValidationError
+from trainingmgr.common.exceptions_utls import DBException
+from trainingmgr.common.trainingmgr_operations import create_dme_filtered_data_job
+from trainingmgr.common.trainingmgr_util import check_trainingjob_name_or_featuregroup_name
+from trainingmgr.db.featuregroup_db import add_featuregroup, delete_feature_group_by_name
+from trainingmgr.common.trainingmgr_config import TrainingMgrConfig
+from trainingmgr.schemas import FeatureGroupSchema
+
+
+
+featuregroup_controller = Blueprint('featuregroup_controller', __name__)
+TRAININGMGR_CONFIG_OBJ = TrainingMgrConfig()
+LOGGER = TRAININGMGR_CONFIG_OBJ.logger
+MIMETYPE_JSON = "application/json"
+
+@featuregroup_controller.route('/featureGroup', methods=['POST'])
+def create_feature_group():
+ """
+ Rest endpoint to create feature group
+
+ Args in function:
+ NONE
+
+ Args in json:
+ json with below fields are given:
+ featureGroupName: str
+ description
+ feature_list: str
+ feature names
+ datalake: str
+ name of datalake
+ bucket: str
+ bucket name
+ host: str
+ db host
+ port: str
+ db port
+ token: str
+ token for the bucket
+ db org: str
+ db org name
+ measurement: str
+ measurement of the influxdb
+ enable_Dme: boolean
+ whether to enable dme
+ source_name: str
+ name of source
+ DmePort: str
+ DME port
+ measured_obj_class: str
+ obj class for dme.
+ datalake_source: str
+ string indicating datalake source
+
+ Returns:
+ 1. For post request
+ json:
+ result : str
+ result message
+ status code:
+ HTTP status code 201
+ 2. For put request
+ json:
+ result : str
+ result message
+ status code:
+ HTTP status code 200
+
+ Exceptions:
+ All exception are provided with exception message and HTTP status code."""
+
+ api_response = {}
+ response_code = status.HTTP_500_INTERNAL_SERVER_ERROR
+ LOGGER.debug('feature Group Create request, ' + json.dumps(request.json))
+
+ try:
+ featuregroup = FeatureGroupSchema().load(request.get_json())
+ feature_group_name = featuregroup.featuregroup_name
+ # check the data conformance
+ # LOGGER.debug("the db info is : ", get_feature_group_by_name_db(PS_DB_OBJ, feature_group_name))
+ if (not check_trainingjob_name_or_featuregroup_name(feature_group_name) or
+ len(feature_group_name) < 3 or len(feature_group_name) > 63):
+ api_response = {"Exception": "Failed to create the feature group since feature group not valid"}
+ response_code = status.HTTP_400_BAD_REQUEST
+ else:
+ # the features are stored in string format in the db, and has to be passed as list of feature to the dme. Hence the conversion.
+ add_featuregroup(featuregroup)
+ api_response = FeatureGroupSchema().dump(featuregroup)
+ response_code =status.HTTP_200_OK
+ if featuregroup.enable_dme == True :
+ response= create_dme_filtered_data_job(TRAININGMGR_CONFIG_OBJ, featuregroup)
+ if response.status_code != 201:
+ api_response={"Exception": "Cannot create dme job"}
+ delete_feature_group_by_name(featuregroup)
+ response_code=status.HTTP_400_BAD_REQUEST
+ except ValidationError as err:
+ LOGGER.error(f"Failed to create the feature Group {str(err)}")
+ return {"Exception": str(err)}, 400
+ except DBException as err:
+ LOGGER.error(f"Failed to create the feature Group {str(err)}")
+ return {"Exception": str(err)}, 400
+ except Exception as e:
+ api_response = {"Exception":str(e)}
+ LOGGER.error(f"Failed to create the feature Group {str(err)}")
+ jsonify(json.dumps(api_response)), 500
+
+ return jsonify(api_response), 201
\ No newline at end of file
#
# ==================================================================================
from . import db
+from sqlalchemy import UniqueConstraint
class FeatureGroup(db.Model):
__tablename__ = "featuregroup_info_table"
dme_port = db.Column(db.String(128), nullable=True)
source_name = db.Column(db.String(20000), nullable=True)
+ __table_args__ = (
+ UniqueConstraint("featuregroup_name", name="unique featuregroup"),
+ )
+
def __repr__(self):
return f'<featuregroup {self.featuregroup_name}>'
\ No newline at end of file
from trainingmgr.schemas import ma, TrainingJobSchema , FeatureGroupSchema
from trainingmgr.db.featuregroup_db import add_featuregroup, edit_featuregroup, get_feature_groups_db, \
get_feature_group_by_name_db, delete_feature_group_by_name
-from trainingmgr.controller.trainingjob_controller import training_job_controller
+from trainingmgr.controller import featuregroup_controller, training_job_controller
from trainingmgr.controller.pipeline_controller import pipeline_controller
from trainingmgr.common.trainingConfig_parser import validateTrainingConfig, getField
from trainingmgr.handler.async_handler import start_async_handler
TRAININGMGR_CONFIG_OBJ = TrainingMgrConfig()
from middleware.loggingMiddleware import LoggingMiddleware
APP.wsgi_app = LoggingMiddleware(APP.wsgi_app)
+APP.register_blueprint(featuregroup_controller)
APP.register_blueprint(training_job_controller)
APP.register_blueprint(pipeline_controller)
status= response_code,
mimetype=MIMETYPE_JSON)
-@APP.route('/featureGroup', methods=['POST'])
-def create_feature_group():
- """
- Rest endpoint to create feature group
-
- Args in function:
- NONE
-
- Args in json:
- json with below fields are given:
- featureGroupName: str
- description
- feature_list: str
- feature names
- datalake: str
- name of datalake
- bucket: str
- bucket name
- host: str
- db host
- port: str
- db port
- token: str
- token for the bucket
- db org: str
- db org name
- measurement: str
- measurement of the influxdb
- enable_Dme: boolean
- whether to enable dme
- source_name: str
- name of source
- DmePort: str
- DME port
- measured_obj_class: str
- obj class for dme.
- datalake_source: str
- string indicating datalake source
-
- Returns:
- 1. For post request
- json:
- result : str
- result message
- status code:
- HTTP status code 201
- 2. For put request
- json:
- result : str
- result message
- status code:
- HTTP status code 200
-
- Exceptions:
- All exception are provided with exception message and HTTP status code."""
-
- api_response = {}
- response_code = status.HTTP_500_INTERNAL_SERVER_ERROR
- LOGGER.debug('feature Group Create request, ' + json.dumps(request.json))
-
- try:
- featuregroup = FeatureGroupSchema().load(request.get_json())
- feature_group_name = featuregroup.featuregroup_name
- # check the data conformance
- # LOGGER.debug("the db info is : ", get_feature_group_by_name_db(PS_DB_OBJ, feature_group_name))
- if (not check_trainingjob_name_or_featuregroup_name(feature_group_name) or
- len(feature_group_name) < 3 or len(feature_group_name) > 63):
- api_response = {"Exception": "Failed to create the feature group since feature group not valid"}
- response_code = status.HTTP_400_BAD_REQUEST
- else:
- # the features are stored in string format in the db, and has to be passed as list of feature to the dme. Hence the conversion.
- add_featuregroup(featuregroup)
- api_response = FeatureGroupSchema().dump(featuregroup)
- response_code =status.HTTP_200_OK
- if featuregroup.enable_dme == True :
- response= create_dme_filtered_data_job(TRAININGMGR_CONFIG_OBJ, featuregroup)
- if response.status_code != 201:
- api_response={"Exception": "Cannot create dme job"}
- delete_feature_group_by_name(featuregroup)
- response_code=status.HTTP_400_BAD_REQUEST
- except ValidationError as err:
- return {"Exception": str(err)}, 400
- except DBException as err:
- return {"Exception": str(err)}, 400
- except Exception as e:
- err_msg = "Failed to create the feature Group "
- api_response = {"Exception":str(e)}
- LOGGER.error(str(e))
-
- return APP.response_class(response=json.dumps(api_response),
- status=response_code,
- mimetype=MIMETYPE_JSON)
@APP.route('/featureGroup', methods=['GET'])
def get_feature_group():
Code Review / aiml-fw / awmf / tm.git / commitdiff