Repository: ADALabUCSD/cerebro-system (language: python)

Issue:
Implement REST API for Cerebro
Cerebro is currently developed as a library.
In order to support use cases such as web UIs we need to implement a REST API.


Discussion / hints:

Fix patch:
diff --git a/cerebro/cerebro_server.py b/cerebro/api/cerebro_server.py
similarity index 67%
rename from cerebro/cerebro_server.py
rename to cerebro/api/cerebro_server.py
index d3815104..ee0fd7ae 100644
--- a/cerebro/cerebro_server.py
+++ b/cerebro/api/cerebro_server.py
@@ -13,29 +13,35 @@
 # limitations under the License.
 # ==============================================================================
 import os
+import sys
 import werkzeug
 werkzeug.cached_property = werkzeug.utils.cached_property
 import argparse
+import datetime
+import signal
+from threading import Thread, Event
+
+from pyspark.sql import SparkSession
+from ..backend import SparkBackend
 
 from flask import Flask, Blueprint
-from .api.restplus import api
-from .database import db
-from .database.models import *
+from .restplus import api
+from ..db import db
+from ..db.dao import *
 
-import logging.config
+from ..tune.daemon import sub_epoch_scheduler
 
 app = Flask(__name__)
-logging_conf_path = os.path.normpath(os.path.join(os.path.dirname(__file__), '../logging.conf'))
-logging.config.fileConfig(logging_conf_path)
-log = logging.getLogger(__name__)
 
-from .api.endpoints.experiments import ns as experiments_namespace
-from .api.endpoints.models import ns as models_namespace
-from .api.endpoints.scripts import ns as scripts_namespace
+from .endpoints.experiments import ns as experiments_namespace
+from .endpoints.models import ns as models_namespace
+from .endpoints.scripts import ns as scripts_namespace
+
 
 def configure_app(flask_app, args):
     flask_app.config['SERVER_NAME'] = args.server_url
     flask_app.config['SPARK_MASTER_URL'] = args.spark_master_url
+    flask_app.config['NUM_WORKERS'] = args.num_workers
     flask_app.config['TEMP_DATA_DIR'] = args.temp_data_dir
     flask_app.config['SCRIPTS_DIR'] = os.path.join(args.temp_data_dir, 'scripts')
 
@@ -73,24 +79,40 @@ def main():
     parser = argparse.ArgumentParser(description='Argument parser for Cerebro API server.')
 
     parser.add_argument('--server-url', help='API server URL.', default='localhost:8888')
-    parser.add_argument('--spark-master-url', help='Spark master URL.', default='spark://localhost:7077')
+    parser.add_argument('--spark-master-url', help='Spark master URL.', default='local[3]')
+    parser.add_argument('--num-workers', help='Cerebro number of workers.', default=3)
     parser.add_argument('--database-uri', help='Database URI.', default='sqlite://')
     parser.add_argument('--temp-data-dir', help='Temp data directory.', default='/tmp')
+    parser.add_argument('--inter-epoch-wait-time', help='Time delay between epoch schedulings', type=int, default=5)
     parser.add_argument('--swagger-ui-doc-expansion', help='Swagger UI doc expansion model.', default='list')
     parser.add_argument('--no-restplus-validation', help='No RESTPlus validation.', default=False, action='store_true')
     parser.add_argument('--restplus-mask-swagger', help='Whether to mask swagger.', default=False, action='store_true')
     parser.add_argument('--restplus-error-404-help', help='Output 404 error help.', default=False, action='store_true')
     parser.add_argument('--debug', help='Run in debug mode.', default=False, action='store_true')
     args = parser.parse_args()
-    
+
     initialize_app(app, args)
-    log.info('>>>>> Starting development server at http://{}/api/ <<<<<'.format(app.config['SERVER_NAME']))
+    print('>>>>> Starting development server at http://{}/api/ <<<<<'.format(app.config['SERVER_NAME']))
     
     with app.app_context():
         db.create_all()
+
+    #intiate the model selection daemon
+    sys.path.append(app.config['SCRIPTS_DIR'])
+    spark = SparkSession.builder.appName("Cerebro Backend").master(app.config['SPARK_MASTER_URL']).getOrCreate()
+    backend = SparkBackend(spark_context=spark.sparkContext, num_workers=app.config['NUM_WORKERS'])
+    
+    # initialize backend and data loaders
+    if args.debug: print('CEREBRO => Time: {}, Initializing Workers'.format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
+    backend.initialize_workers()
+    app.config['CEREBRO_BACKEND'] = backend
     
+    sub_epoch_scheduler_thread = Thread(target=sub_epoch_scheduler, args=(app, db, backend, args.inter_epoch_wait_time, 2 if args.debug == True else 0,))
+    sub_epoch_scheduler_thread.start()
+
     app.run(debug=args.debug)
+    sub_epoch_scheduler_thread.join()
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/cerebro/api/endpoints/experiments.py b/cerebro/api/endpoints/experiments.py
index d23513e1..763532d4 100644
--- a/cerebro/api/endpoints/experiments.py
+++ b/cerebro/api/endpoints/experiments.py
@@ -12,15 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
+import sys
+import time
 import logging
-
+import traceback
+from werkzeug.exceptions import BadRequest
 from flask import request
 from flask_restplus import Resource
+from pyspark.sql import SparkSession
+from importlib import import_module
+from threading import Thread, Lock
+from sqlalchemy import and_
+
 from ..restplus import api
 from ..serializers import experiment
+from ...db.dao import Experiment, ParamDef, Model, ParamVal
+from ...db import db
+from ..cerebro_server import app
+from ...commons.constants import *
 
-log = logging.getLogger(__name__)
+from ...backend import SparkBackend
+from ...storage import LocalStore, HDFSStore
+from ...tune import TPESearch, hp_choice, hp_uniform, hp_quniform, hp_loguniform, hp_qloguniform
+from ...tune.grid import HILGridSearch, HILRandomSearch
 
 ns = api.namespace('experiments', description='Operations related to experiments')
 
@@ -32,16 +46,60 @@ def get(self):
         """
         Returns list of experiments.
         """
-        raise NotImplementedError()
+        return Experiment.query.all()
+
 
     @api.expect(experiment)
-    @api.marshal_with(str, code=201)
+    @api.response(201, 'Experiment successfully created.')
     def post(self):
         """
         Creates a new experiment.
         """
-        raise NotImplementedError()
-        #return None, 201
+        data = request.json
+        name = data.get('name')
+        description = data.get('description')
+        model_selection_algorithm = data.get('model_selection_algorithm')
+        max_num_models = data.get('max_num_models')
+        feature_columns = data.get('feature_columns')
+        label_columns =  data.get('label_columns')
+        max_train_epochs = data.get('max_train_epochs')
+        data_store_prefix_path =  data.get('data_store_prefix_path')
+        executable_entrypoint = data.get('executable_entrypoint')
+
+        # Experiment validation
+        if model_selection_algorithm in [MS_RANDOM_SEARCH, MS_HYPEROPT_SEARCH]:
+            assert max_num_models is not None and max_num_models > 0, '{} should have valid max_num_models value'.format(model_selection_algorithm)
+
+        exp_dao = Experiment(name, description, model_selection_algorithm, max_num_models, feature_columns, label_columns, max_train_epochs,
+            data_store_prefix_path, executable_entrypoint)
+        db.session.add(exp_dao)
+
+        for pdef in data.get('param_defs'):
+            name = pdef.get('name')
+            param_type = pdef.get('param_type')
+            choices = pdef.get('choices')
+            min = pdef.get('min')
+            max = pdef.get('max')
+            q = pdef.get('q')
+            dtype = pdef.get('dtype')
+
+            # Parameter validation
+            if param_type == HP_CHOICE:
+                assert choices is not None, '{} should have non null choices'.format(param_type)
+            if param_type in [HP_LOGUNIFORM, HP_QLOGUNIFORM, HP_QUNIFORM, HP_UNIFORM]:
+                assert min is not None and max is not None, '{} should have non null min/max values'.format(param_type)
+            if param_type in [HP_QLOGUNIFORM, HP_QUNIFORM]:
+                assert q is not None, '{} should have non null q value'.format(param_type)
+
+            pdef_dao = ParamDef(exp_dao.id, name, param_type, choices, min, max, q, dtype)
+            db.session.add(pdef_dao)
+
+        db.session.commit()        
+
+        thread = Thread(target=experiment_runner, args=(exp_dao.id, app,))
+        thread.start()
+
+        return exp_dao.id, 201
 
 
 @ns.route('/<string:id>')
@@ -53,13 +111,88 @@ def get(self, id):
         """
         Returns an experiment with all its models.
         """
-        raise NotImplementedError()
+        return Experiment.query.filter(Experiment.id == id).one()
 
 
-    @api.response(204, 'Experiment successfully deleted.')
-    def delete(self, id):
-        """
-        Deletes experiment.
-        """
-        raise NotImplementedError()
-        # return None, 204
+def experiment_runner(exp_id, app):
+    with app.app_context():
+        exp_obj = Experiment.query.filter(Experiment.id == exp_id).one()
+        
+        try:
+            # Initial experiment creation.
+            if exp_obj.status == CREATED_STATUS:
+                search_space = {}
+                param_defs = ParamDef.query.filter(ParamDef.exp_id == exp_id).all()
+                for param_def in param_defs:
+                    param_name = param_def.name
+                    param_vals = []
+
+                    if param_def.param_type == HP_CHOICE:
+                        param_vals = [x.strip()  for x in param_def.choices.split(',')]
+                        if param_def.dtype == DTYPE_FLOAT:
+                            param_vals = [float(x) for x in param_vals]
+                        elif param_def.dtype == DTYPE_INT:
+                            param_vals = [int(x) for x in param_vals]
+
+                        search_space[param_name] = hp_choice(param_vals)
+                    else:
+                        min_val = param_def.min
+                        max_val = param_def.max
+                        if param_def.dtype == DTYPE_FLOAT:
+                            min_val = float(min_val)
+                            max_val = float(max_val)
+                        elif param_def.dtype == DTYPE_INT:
+                            min_val = int(min_val)
+                            max_val = int(max_val)
+
+                        q = None
+                        if param_def.param_type in [HP_QUNIFORM, HP_QLOGUNIFORM]:
+                            q = param_def.q
+                            if param_def.dtype == DTYPE_FLOAT:
+                                q = float(q)
+                            elif param_def.dtype == DTYPE_INT:
+                                q = int(q)
+
+                        if param_def.param_type == HP_UNIFORM:
+                            search_space[param_name] = hp_uniform(min_val, max_val)
+                        elif param_def.param_type == HP_LOGUNIFORM:
+                            search_space[param_name] = hp_loguniform(min_val, max_val)
+                        elif param_def.param_type == HP_QUNIFORM:
+                            search_space[param_name] = hp_quniform(min_val, max_val, q)
+                        elif param_def.param_type == HP_QLOGUNIFORM:
+                            search_space[param_name] = hp_qloguniform(min_val, max_val, q)
+                        else:
+                            raise NotImplementedError('Unsupported hyperparameter type: {}'.format(param_def.param_type))
+
+                backend = app.config['CEREBRO_BACKEND']
+                data_store_prefix_path = exp_obj.data_store_prefix_path
+                if data_store_prefix_path.startswith('hdfs://'):
+                    store = HDFSStore(prefix_path=data_store_prefix_path)
+                else:
+                    store = LocalStore(prefix_path=data_store_prefix_path)
+
+                if exp_obj.model_selection_algorithm == MS_GRID_SEARCH:
+                    model_selection = HILGridSearch(
+                        exp_id=exp_obj.id, backend=backend, store=store, estimator_gen_fn=None, search_space=search_space,
+                        num_epochs=int(exp_obj.max_train_epochs), db=db, verbose=2 if app.config['DEBUG'] else 0
+                    )
+                elif exp_obj.model_selection_algorithm == MS_RANDOM_SEARCH:
+                    model_selection = HILRandomSearch(
+                        exp_id=exp_obj.id, backend=backend, store=store, estimator_gen_fn=None, search_space=search_space,
+                        num_models=int(exp_obj.max_num_models), num_epochs=int(exp_obj.max_train_epochs), db=db, verbose=2 if app.config['DEBUG'] else 0
+                    )
+                elif exp_obj.model_selection_algorithm == MS_HYPEROPT_SEARCH:
+                    # TODO
+                    raise NotImplementedError()
+
+                model_selection.fit_on_prepared_data()
+                
+                db.session.refresh(exp_obj)
+                exp_obj.status = COMPLETED_STATUS
+                db.session.commit()
+        except Exception as e:
+            logging.error(traceback.format_exc())
+            db.session.refresh(exp_obj)
+            exp_obj.status = FAILED_STATUS
+            exp_obj.exception_message = str(traceback.format_exc())
+            db.session.commit()
diff --git a/cerebro/api/endpoints/models.py b/cerebro/api/endpoints/models.py
index 2d1f8024..b9eda47b 100644
--- a/cerebro/api/endpoints/models.py
+++ b/cerebro/api/endpoints/models.py
@@ -12,16 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import logging
-
 from flask import request
 from flask_restplus import Resource
+from flask_restplus import abort
+from werkzeug.exceptions import BadRequest
+from sqlalchemy import and_
 from ..restplus import api
 from ..serializers import model
 from ..parsers import experiment_id_argument
-
-
-log = logging.getLogger(__name__)
+from ...db.dao import Model, Experiment, ParamVal, ParamDef
+from ...db import db
+from ...commons.constants import *
+from .experiments import next_user_friendly_model_id
 
 ns = api.namespace('models', description='Operations related to models')
 
@@ -34,34 +36,94 @@ def get(self):
         """
         Returns list of models.
         """
-        raise NotImplementedError()
+        args = experiment_id_argument.parse_args()
+        exp_id = args['exp_id']
+        return Model.query.filter(Model.exp_id == exp_id).all()
 
     @api.expect(model)
-    @api.marshal_with(str, code=201)
     def post(self):
         """
         Creates a new model.
         """
-        raise NotImplementedError()
-        #return None, 201
+        data = request.json
+        exp_id = data.get('exp_id')
+        num_trained_epochs =  0
+        max_train_epochs = data.get('max_train_epochs')
+        warm_start_model_id = data.get('warm_start_model_id')
+
+        if warm_start_model_id is not None:
+            warm_start_model = Model.query.filter(Model.id == warm_start_model_id).one()
+            print(warm_start_model.status, warm_start_model.num_trained_epochs)
+            assert warm_start_model.status in [RUNNING_STATUS, COMPLETED_STATUS] and warm_start_model.num_trained_epochs > 0, \
+            'Warm start model should be a completed or current running model with atleast 1 epoch trained.'
+
+        exp = Experiment.query.filter(Experiment.id == exp_id).one()
+        if exp.status in [FAILED_STATUS, STOPPED_STATUS, COMPLETED_STATUS]:
+            raise BadRequest('Experiment is in {} staus. Cannot create new models.'.format(exp.status))
+
+        model_id = next_user_friendly_model_id()
+        model_dao = Model(model_id, exp_id, num_trained_epochs, max_train_epochs, warm_start_model_id)
+
+        for pval in data.get('param_vals'):
+            name = pval.get('name')
+            value = pval.get('value')
+
+            dtype = ParamDef.query.filter(and_(ParamDef.exp_id == exp_id, ParamDef.name == name)).one().dtype
+
+            pval_dao = ParamVal(model_dao.id, name, value, dtype)
+            db.session.add(pval_dao)
+
+        db.session.add(model_dao)
+        db.session.commit()
+        return model_dao.id, 201
 
 
 @ns.route('/<string:id>')
 @api.response(404, 'Model not found.')
-class ExperimentItem(Resource):
+class GetModel(Resource):
     
     @api.marshal_with(model)
     def get(self, id):
         """
         Returns a model.
         """
-        raise NotImplementedError()
+        return Model.query.filter(Model.id == id).one()
 
 
-    @api.response(204, 'Model successfully deleted.')
-    def delete(self, id):
+@ns.route('/stop/<string:id>')
+@api.response(404, 'Model not found.')
+@api.response(400, 'Invalid request.')
+class StopModel(Resource):
+    
+    @api.response(204, 'Model successfully stopped.')
+    def post(self, id):
+        """
+        Stops a model.
+        """
+        model = Model.query.filter(Model.id == id).one()
+        if model.status == RUNNING_STATUS:
+            model.status = STOPPED_STATUS
+            db.session.commit()
+            return None, 204
+        else:
+            abort(400, 'Model cannot be stopped from {} status'.format(model.status))
+
+
+@ns.route('/resume/<string:id>')
+@api.response(404, 'Model not found.')
+@api.response(400, 'Invalid request.')
+class ResumeModel(Resource):
+    
+    @api.response(204, 'Model successfully resumed.')
+    def post(self, id):
         """
-        Deletes model.
+        Resumes a model.
         """
-        raise NotImplementedError()
-        # return None, 204
+        model = Model.query.filter(Model.id == id).one()
+        exp = Experiment.query.filter(Experiment.id == model.exp_id).one()
+        if model.status == STOPPED_STATUS and exp.status == RUNNING_STATUS:
+            model.status = RUNNING_STATUS
+            db.session.commit()
+            return None, 204
+        else:
+            abort(400, 'Model cannot be resumed from {} status'.format(model.status))
diff --git a/cerebro/api/endpoints/scripts.py b/cerebro/api/endpoints/scripts.py
index 32a09c58..b044c27d 100644
--- a/cerebro/api/endpoints/scripts.py
+++ b/cerebro/api/endpoints/scripts.py
@@ -13,17 +13,15 @@
 # limitations under the License.
 # ==============================================================================
 import os
-import logging
 import werkzeug
 import shutil
 from flask import request
 from flask_restplus import Resource
 from ..restplus import api
 from ..parsers import upload_parser
-from ...database import db
-from ...cerebro_server import app
+from ...db import db
+from ..cerebro_server import app
 
-log = logging.getLogger(__name__)
 
 ns = api.namespace('scripts', description='Operations related to uploading model creation scripts')
 
@@ -52,4 +50,4 @@ def delete(self):
         """
         shutil.rmtree(app.config['SCRIPTS_DIR'])
         os.makedirs(app.config['SCRIPTS_DIR'])
-        return None, 204
\ No newline at end of file
+        return None, 204
diff --git a/cerebro/api/restplus.py b/cerebro/api/restplus.py
index d9910f1a..74c609cd 100644
--- a/cerebro/api/restplus.py
+++ b/cerebro/api/restplus.py
@@ -17,15 +17,17 @@
 import traceback
 
 from flask_restplus import Api
-
-log = logging.getLogger(__name__)
+from sqlalchemy.orm.exc import NoResultFound
 
 api = Api(version='1.0', title='Cerebro REST API')
 
-
 @api.errorhandler
 def default_error_handler(e):
     message = 'An unhandled exception occurred.'
-    log.exception(message)
+    print(message)
     return {'message': message}, 500
 
+@api.errorhandler(NoResultFound)
+def database_not_found_error_handler(e):
+    print(traceback.format_exc())
+    return {'message': 'A database result was required but none was found.'}, 404
diff --git a/cerebro/api/serializers.py b/cerebro/api/serializers.py
index dce920e7..7f69d168 100644
--- a/cerebro/api/serializers.py
+++ b/cerebro/api/serializers.py
@@ -15,44 +15,52 @@
 
 from flask_restplus import fields
 from .restplus import api
+from ..commons.constants import *
+
+state_enums = [CREATED_STATUS, RUNNING_STATUS, FAILED_STATUS, STOPPED_STATUS, CREATED_STATUS]
+param_type_enums = [HP_CHOICE, HP_LOGUNIFORM, HP_QLOGUNIFORM, HP_QUNIFORM, HP_UNIFORM]
+param_dtype_enums = [DTYPE_STR, DTYPE_INT, DTYPE_FLOAT]
+model_selection_algo_enums = [MS_GRID_SEARCH, MS_RANDOM_SEARCH, MS_HYPEROPT_SEARCH]
 
 
 ############### Hyperparameters #################
 param_def = api.model('ParamDef', {
-    # 'id': fields.String(readonly=True, description='Hyperparameter definition UID'),
     'name': fields.String(required=True, description='Hyperparameter name'),
-    'param_type': fields.String(required=True, description='Hyperparameter type', enum=['categorical', 'int', 'float', 'log']),
-    'values': fields.List(fields.String, required=False, description='Categorical values in the case of a categorical hparam type'),
-    'min_val': fields.Float(required=False, description='Minimum value used for int, float, and log hparam types'),
-    'max_val': fields.Float(required=False, description='Maximum value used for int, float, and log hparam types'),
-    'count': fields.Integer(required=False, description='Number of values to be taken from the range [min, max]. For int and float values are evenly chosen. For log values are chosen logarithmically evenly'),
-    'base': fields.Integer(required=False, description='Base value for log hparam type'),
+    'param_type': fields.String(required=True, description='Hyperparameter type', enum=param_type_enums),
+    'choices': fields.String(required=False, description='Comma separated list of values in the case of a {} hparam type'.format(HP_CHOICE)),
+    'min': fields.Float(required=False, description='Minimum value'),
+    'max': fields.Float(required=False, description='Maximum value'),
+    'q': fields.Float(required=False, description='Quantum'),
+    'dtype': fields.String(required=True, description='Parameter dtype', enum=param_dtype_enums, default=DTYPE_STR)
 })
 
 param_val = api.model('ParamVal', {
-    # 'id': fields.String(readonly=True, description='Hyperparameter value UID'),
     'name': fields.String(required=True, description='Hyperparameter name'),
     'value': fields.String(required=True, description='Hyperparameter value'),
-    'value_type': fields.String(required=True, description='Hyperparameter value type', enum=['str', 'int', 'float'])
+    'dtype': fields.String(readonly=True, description='Hyperparameter value dtype')
 })
 
 #################### Metrics ####################
 metric = api.model('Metric', {
     'name': fields.String(readonly=True, description='Metric name'),
-    'metric_type': fields.String(readonly=True, description='Metric type', enum=['train', 'validation']),
-    'values': fields.List(fields.Float, readonly=True, description='Metric values list for every epoch')
+    'values': fields.String(readonly=True, description='Comma separated list of metric values list for every epoch')
 })
 
 
 #################### Model ######################
 model = api.model('Model', {
     'id': fields.String(readonly=True, description='Model UID'),
-    'experiment_id': fields.String(required=True, description='Experiment UID'),
+    'name': fields.String(readonly=True, description='Userfriendly model name'),
+    'exp_id': fields.String(required=True, description='Experiment UID'),
     'creation_time': fields.DateTime(readonly=True, description='Experiment creation time'),
     'last_update_time': fields.DateTime(readonly=True, description='Experiment last update time'),
-    'status': fields.String(readonly=True, description='Model status', enum=['created', 'running', 'failed', 'completed']),
+    'status': fields.String(readonly=True, description='Model status', enum=state_enums),
+    'num_trained_epochs': fields.Integer(readonly=True, description='Current number of trained epochs for the model'),
+    'max_train_epochs': fields.Integer(required=True, description='Maximum number of training epochs for the model'),
+    'warm_start_model_id': fields.String(required=False, description='UID of the warm starting model in the case of a model clone'),
     'param_vals': fields.List(fields.Nested(param_val), required=True, description='Hyperparameter values'),
-    'metrics': fields.List(fields.Nested(metric), readonly=True, description='Model training metrics')
+    'metrics': fields.List(fields.Nested(metric), readonly=True, description='Model training metrics'),
+    'exception_message': fields.String(readonly=True, description='Exception message in the case of an model initialization failure')
 })
 
 
@@ -63,12 +71,15 @@
     'description': fields.String(description='Experiment description'),
     'creation_time': fields.DateTime(readonly=True, description='Experiment creation time'),
     'last_update_time': fields.DateTime(readonly=True, description='Experiment last update time'),
-    'status': fields.String(readonly=True, description='Experiment status', enum=['created', 'running', 'failed', 'completed']),
+    'status': fields.String(readonly=True, description='Experiment status', enum=state_enums),
+    'model_selection_algorithm': fields.String(required=True, description='Model selection algorithm', enum=model_selection_algo_enums),
+    'max_num_models': fields.Integer(required=False, description='Maximum number of models to be explored by the model selection algorithm. Needed for RandomSearch, HyperOpt'),
     'param_defs': fields.List(fields.Nested(param_def), required=True, description='Hyperparameter definitions'),
-    'feature_columns': fields.List(fields.String, required=True, description='Training data feature columns names'),
-    'label_columns': fields.List(fields.String, required=True, description='Training data label column names'),
+    'feature_columns': fields.String(required=True, description='Comma separated list of training data feature columns names'),
+    'label_columns': fields.String(required=True, description='Comma separated list of training data label column names'),
     'max_train_epochs': fields.Integer(required=True, description='Maximum number of training epochs for any model'),
-    'training_data_prefix_path': fields.String(required=True, description='Training data prefix path'),
+    'data_store_prefix_path': fields.String(required=True, description='Data store prefix path'),
     'executable_entrypoint': fields.String(required=True, description='Estimator generator function in the form of <module_name>:<function_name>'),
-    'models': fields.List(fields.Nested(model), readonly=True, description='Models in this experiment')
+    'models': fields.List(fields.Nested(model), readonly=True, description='Models in this experiment'),
+    'exception_message': fields.String(readonly=True, description='Exception message in the case of an experiment failure')
 })
diff --git a/cerebro/backend/backend.py b/cerebro/backend/backend.py
index 482a14c1..c0c12aa4 100644
--- a/cerebro/backend/backend.py
+++ b/cerebro/backend/backend.py
@@ -32,19 +32,18 @@ def initialize_workers(self):
         """Initialize workers"""
         raise NotImplementedError()
 
-    def initialize_data_loaders(self, store, dataset_idx, schema_fields):
+    def initialize_data_loaders(self, store, schema_fields):
         """Initialize data loaders"""
         raise NotImplementedError()
 
-    def train_for_one_epoch(self, models, store, dataset_idx, feature_col, label_col, is_train=True):
+    def train_for_one_epoch(self, models, store, feature_col, label_col, is_train=True):
         """
         Takes a set of Keras models and trains for one epoch. If is_train is False, validation is performed
          instead of training.
         :param models:
-        :param store:
-        :param dataset_idx:
-        :param feature_col:
-        :param label_col:
+        :param store: single store object common for all models or a dictionary of store objects indexed by model id.
+        :param feature_col: single list of feature columns common for all models or a dictionary of feature lists indexed by model id.
+        :param label_col: single list of label columns common for all models or a dictionary of label lists indexed by model id.
         :param is_train:
         """
         raise NotImplementedError()
diff --git a/cerebro/backend/spark/backend.py b/cerebro/backend/spark/backend.py
index f0840cc3..9ca08a17 100644
--- a/cerebro/backend/spark/backend.py
+++ b/cerebro/backend/spark/backend.py
@@ -35,6 +35,7 @@
 from .. import timeout, settings as spark_settings, secret, host_hash, job_id
 from ..backend import Backend
 from ...commons.util import patch_hugginface_layer_methods
+from ...commons.constants import exit_event
 
 PETASTORM_HDFS_DRIVER = constants.PETASTORM_HDFS_DRIVER
 TOTAL_BUFFER_MEMORY_CAP_GIB = constants.TOTAL_BUFFER_MEMORY_CAP_GIB
@@ -57,7 +58,7 @@ def on_train_batch_begin(self, batch, logs={}):
 
     def on_test_batch_begin(self, batch, logs={}):
         self.counter += 1
-
+    
     def get_step_count(self):
         return self.counter
 
@@ -155,29 +156,29 @@ def initialize_workers(self):
         self.spark_job_group = spark_job_group
         self.workers_initialized = True
 
-    def initialize_data_loaders(self, store, dataset_idx, schema_fields):
+    def initialize_data_loaders(self, store, schema_fields):
         """
         :param store:
         :param dataset_idx:
         :param schema_fields:
         """
         if self.workers_initialized:
-            remote_store = store.to_remote(self.spark_job_group, dataset_idx)
+            remote_store = store.to_remote(self.spark_job_group, None)
             shard_count = self._num_workers()
-            _, _, _, avg_row_size = util.get_simple_meta_from_parquet(store, schema_fields, None, dataset_idx)
+            _, _, _, avg_row_size = util.get_simple_meta_from_parquet(store, schema_fields, None, None)
             data_readers_fn = _data_readers_fn(remote_store, shard_count, schema_fields, avg_row_size,
                                                self.settings.disk_cache_size_bytes,
                                                self.settings.data_readers_pool_type, self.settings.num_data_readers)
 
             for task_client in self.task_clients:
-                task_client.initialize_data_loaders(data_readers_fn)
+                task_client.initialize_data_loaders(store.prefix_path, data_readers_fn)
 
             self.data_loaders_initialized = False
         else:
             raise Exception('Spark tasks not initialized for Cerebro. Please run SparkBackend.initialize_workers() '
                             'first!')
 
-    def train_for_one_epoch(self, models, store, dataset_idx, feature_col, label_col, is_train=True):
+    def train_for_one_epoch(self, models, store, feature_cols, label_cols, is_train=True):
 
         mode = "Training"
         if not is_train:
@@ -185,9 +186,24 @@ def train_for_one_epoch(self, models, store, dataset_idx, feature_col, label_col
         if self.settings.verbose >= 1:
             print('CEREBRO => Time: {}, Starting EPOCH {}'.format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), mode))
 
-        sub_epoch_trainers = [_get_remote_trainer(model, self, store, dataset_idx, feature_col, label_col,
-                                                  is_train, self.settings.verbose) \
-                              for model in models]
+        sub_epoch_trainers = []
+        for model in models:
+            if type(store) == dict:
+                a_store = store[model.getRunId()]
+            else:
+                a_store = store
+            
+            if type(feature_cols) == dict:
+                a_feature_col = feature_cols[model.getRunId()]
+            else:
+                a_feature_col = feature_cols
+            
+            if type(label_cols) == dict:
+                a_label_col = label_cols[model.getRunId()]
+            else:
+                a_label_col = label_cols
+            
+            sub_epoch_trainers.append(_get_remote_trainer(model, self, a_store, None, a_feature_col, a_label_col, is_train, self.settings.verbose))
 
         model_worker_pairs = [(i, j) for i in range(len(models)) for j in range(self._num_workers())]
         # take a random ordering
@@ -200,7 +216,7 @@ def train_for_one_epoch(self, models, store, dataset_idx, feature_col, label_col
         model_results = {model.getRunId(): None for model in models}
         model_sub_epoch_steps = {model.getRunId(): None for model in models}
 
-        while len(model_worker_pairs) > 0:
+        while not exit_event.is_set() and len(model_worker_pairs) > 0:
 
             for w in range(self._num_workers()):
                 # worker idle
@@ -211,8 +227,14 @@ def train_for_one_epoch(self, models, store, dataset_idx, feature_col, label_col
                         if self.settings.verbose >= 1:
                             print('CEREBRO => Time: {}, Scheduling Model: {}, on Worker: {}'.format(
                                 datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), models[m].getRunId(), w))
+                        
+                        if type(store) == dict:
+                            a_store = store[models[m].getRunId()]
+                        else:
+                            a_store = store
+
                         self.task_clients[w].execute_sub_epoch(
-                            fn=sub_epoch_trainers[m], train=is_train, initial_epoch=models[m].getEpochs())
+                            fn=sub_epoch_trainers[m], store_prefix_path=a_store.prefix_path, train=is_train, initial_epoch=models[m].getEpochs())
 
                         model_states[m] = True
                         worker_states[w] = True
@@ -251,7 +273,7 @@ def train_for_one_epoch(self, models, store, dataset_idx, feature_col, label_col
                                 print('CEREBRO => Time: {}, Completed Model: {}, on Worker: {}'.format(
                                     datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), models[m].getRunId(), w))
 
-            time.sleep(self.settings.polling_period)
+            exit_event.wait(self.settings.polling_period)
 
         # incrementing the model epoch number
         if is_train:
@@ -338,29 +360,14 @@ def _get_remote_trainer(estimator, backend, store, dataset_idx, feature_columns,
     keras_utils = estimator._get_keras_utils()
 
     # Checkpointing the model if it does not exist.
-    if not estimator._has_checkpoint(run_id) or (is_train and estimator.getModelUpdateFn() is not None):
+    if not estimator._has_checkpoint(run_id):
         remote_store = store.to_remote(run_id, dataset_idx)
-        
-        # Update the model by passing it to the user provided model update function.
-        if estimator.getModelUpdateFn() is not None:
-            # Load existing checkpoint if it already exists.
-            if estimator._has_checkpoint(run_id):
-                with tf.keras.utils.custom_object_scope(estimator.getCustomObjects()):
-                    model = _deserialize_keras_model_fn()(
-                        remote_store.get_last_checkpoint(), lambda x: tf.keras.models.load_model(x))
-            else:
-                model = estimator.getModel()
-
-            hyper_params = estimator.getHyperParams()
-            epoch = estimator.getEpochs() + 1
-            model = estimator.getModelUpdateFn()(model, epoch, hyper_params)
-            estimator.setModel(model)
 
         if verbose >= 2:
             print('CEREBRO => Time: {}, Checkpointing artifacts for Model: {}'.format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), run_id))
 
-        model = estimator._compile_model(keras_utils)
         with remote_store.get_local_output_dir() as run_output_dir:
+            model = estimator._compile_model(keras_utils)
             ckpt_file = os.path.join(run_output_dir, remote_store.checkpoint_filename)
             model.save(ckpt_file)
             remote_store.sync(run_output_dir)
@@ -378,7 +385,7 @@ def _data_readers(index):
 
         PETASTORM_HDFS_DRIVER = constants.PETASTORM_HDFS_DRIVER
 
-        train_reader = make_reader(remote_store.train_data_path, shuffle_row_groups=False, num_epochs=None,
+        train_reader = make_reader(remote_store.train_data_path, shuffle_row_groups=False, num_epochs=1,
                                    cur_shard=index,
                                    shard_count=shard_count,
                                    hdfs_driver=PETASTORM_HDFS_DRIVER,
@@ -390,7 +397,7 @@ def _data_readers(index):
                                    cache_extra_settings={'cleanup': True})
 
         if remote_store.val_data_path != '' and remote_store.val_data_path is not None:
-            val_reader = make_reader(remote_store.val_data_path, shuffle_row_groups=False, num_epochs=None,
+            val_reader = make_reader(remote_store.val_data_path, shuffle_row_groups=False, num_epochs=1,
                                      cur_shard=index,
                                      shard_count=shard_count,
                                      hdfs_driver=PETASTORM_HDFS_DRIVER,
@@ -501,7 +508,6 @@ def train(data_reader, is_train, starting_epoch, local_task_index=0):
             if issubclass(custom_objects[k], tf.keras.layers.Layer) and inspect.getmodule(custom_objects[k]).__name__.startswith('transformers.'):
                 patch_hugginface_layer_methods(custom_objects[k])
 
-
         tf.keras.backend.set_floatx(floatx)
         pin_gpu(local_task_index)
 
@@ -531,13 +537,6 @@ def train(data_reader, is_train, starting_epoch, local_task_index=0):
                 model = deserialize_keras_model(
                     remote_store.get_last_checkpoint(), lambda x: tf.keras.models.load_model(x))
 
-            steps_per_epoch = int(math.ceil(train_rows / batch_size / num_workers))
-
-            # math.ceil because if val_rows is smaller than batch_size we still get the at least
-            # one step. float(val_rows) because val_rows/batch_size evaluates to zero before
-            # math.ceil
-            validation_steps = int(math.ceil(float(val_rows) / batch_size / num_workers))
-
             schema_fields = feature_columns + label_columns
             if sample_weight_col:
                 schema_fields.append(sample_weight_col)
@@ -546,8 +545,7 @@ def train(data_reader, is_train, starting_epoch, local_task_index=0):
                 train_data = make_dataset(data_reader, shuffle_buffer_size, shuffle=False)
                 initialization_time = time.time() - begin_time
                 begin_time = time.time()
-                result = fit_sub_epoch_fn(starting_epoch, model, train_data, steps_per_epoch, callbacks,
-                                          verbose).history
+                result = fit_sub_epoch_fn(starting_epoch, model, train_data, callbacks, verbose).history
                 training_time = time.time() - begin_time
                 begin_time = time.time()
                 result = {'train_' + name: result[name] for name in result}
@@ -556,7 +554,7 @@ def train(data_reader, is_train, starting_epoch, local_task_index=0):
                 val_data = make_dataset(data_reader, shuffle_buffer_size, shuffle=False)
                 initialization_time = time.time() - begin_time
                 begin_time = time.time()
-                result = eval_sub_epoch_fn(starting_epoch, model, val_data, validation_steps, callbacks, verbose)
+                result = eval_sub_epoch_fn(starting_epoch, model, val_data, callbacks, verbose)
                 training_time = time.time() - begin_time
                 begin_time = time.time()
                 result = [[x] for x in result]
@@ -575,6 +573,7 @@ def train(data_reader, is_train, starting_epoch, local_task_index=0):
                       'Finalization Time: {}'.format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                         run_id, 'TRAIN' if is_train else 'VALID', initialization_time, training_time, finalization_time))
 
+            data_reader.reset()
             return result, step_counter_callback.get_step_count()
 
     return train
diff --git a/cerebro/backend/spark/service_task.py b/cerebro/backend/spark/service_task.py
index 952fca3c..7fa393ff 100644
--- a/cerebro/backend/spark/service_task.py
+++ b/cerebro/backend/spark/service_task.py
@@ -34,13 +34,15 @@ def __init__(self, local_task_index):
 
 
 class InitDataLoadersRequest(object):
-    def __init__(self, initialize_data_loaders_fn):
+    def __init__(self, store_prefix_path, initialize_data_loaders_fn):
+        self.store_prefix_path = store_prefix_path
         self.initialize_data_loaders_fn = initialize_data_loaders_fn
 
 
 class ExecuteSubEpochRequest(object):
-    def __init__(self, sub_epoch_fn, train, initial_epoch):
+    def __init__(self, sub_epoch_fn, store_prefix_path, train, initial_epoch):
         self.sub_epoch_fn = sub_epoch_fn
+        self.store_prefix_path = store_prefix_path
         self.is_train = train
         self.initial_epoch = initial_epoch
 
@@ -98,8 +100,8 @@ def __init__(self, index, key, nics):
         self._sub_epoch_thread = None
         self._sub_epoch_status = None
 
-        self._train_reader = None
-        self._val_reader = None
+        self._train_readers = {}
+        self._val_readers = {}
 
     def _make_handler(self):
         server = self
@@ -166,7 +168,11 @@ def _handle(self, req, client_address):
         if isinstance(req, InitDataLoadersRequest):
             self._wait_cond.acquire()
             try:
-                self._train_reader, self._val_reader = req.initialize_data_loaders_fn(self._index)
+                store_prefix_path = req.store_prefix_path
+                if store_prefix_path not in self._train_readers:
+                    train_reader, val_reader = req.initialize_data_loaders_fn(self._index)
+                    self._train_readers[store_prefix_path] = train_reader
+                    self._val_readers[store_prefix_path] = val_reader
             finally:
                 self._wait_cond.notify_all()
                 self._wait_cond.release()
@@ -182,13 +188,13 @@ def _handle(self, req, client_address):
                 if self._sub_epoch_thread is None or not self._sub_epoch_thread.is_alive():
                     self._sub_epoch_status = None
 
-                    def bg_execute(fn, is_train, initial_epoch):
+                    def bg_execute(fn, store_prefix_path, is_train, initial_epoch):
                         try:
                             self._sub_epoch_status = {"status": "RUNNING", "result": None}
                             if is_train:
-                                reader = self._train_reader
+                                reader = self._train_readers[store_prefix_path]
                             else:
-                                reader = self._val_reader
+                                reader = self._val_readers[store_prefix_path]
                             func_result = fn(reader, is_train, initial_epoch,
                                              local_task_index=self.local_task_index)
                             self._sub_epoch_status = {"status": "COMPLETED", "result": func_result}
@@ -196,7 +202,7 @@ def bg_execute(fn, is_train, initial_epoch):
                             self._sub_epoch_status = {"status": "FAILED", "result": None,
                                                       "error": str(e) + "\n" + traceback.format_exc()}
 
-                    self._sub_epoch_thread = threading.Thread(target=bg_execute, args=(req.sub_epoch_fn, req.is_train,
+                    self._sub_epoch_thread = threading.Thread(target=bg_execute, args=(req.sub_epoch_fn, req.store_prefix_path, req.is_train,
                                                                                        req.initial_epoch))
                     self._sub_epoch_thread.start()
             finally:
@@ -372,11 +378,11 @@ def notify_initial_registration_complete(self):
     def notify_workload_complete(self):
         self._send(NotifyWorkloadCompleteRequest())
 
-    def initialize_data_loaders(self, fn):
-        self._send(InitDataLoadersRequest(fn))
+    def initialize_data_loaders(self, store_prefix_path, fn):
+        self._send(InitDataLoadersRequest(store_prefix_path, fn))
 
-    def execute_sub_epoch(self, fn, train=True, initial_epoch=0):
-        self._send(ExecuteSubEpochRequest(fn, train, initial_epoch))
+    def execute_sub_epoch(self, fn, store_prefix_path, train=True, initial_epoch=0):
+        self._send(ExecuteSubEpochRequest(fn, store_prefix_path, train, initial_epoch))
 
     def sub_epoch_completed(self):
         return self._send(SubEpochCompletedRequest())
diff --git a/cerebro/commons/constants.py b/cerebro/commons/constants.py
new file mode 100644
index 00000000..1a12f4c5
--- /dev/null
+++ b/cerebro/commons/constants.py
@@ -0,0 +1,69 @@
+# Copyright 2021 Supun Nakandala, and Arun Kumar. 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 os
+import time
+import random
+import signal
+from threading import Lock, Event
+
+LOCK = Lock()
+MODEL_ID = -1
+
+MODEL_NAMES = [l.strip().lower().replace(' ', '_') for l in open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'model_names.txt')).readlines()]
+random.shuffle(MODEL_NAMES)
+
+def next_user_friendly_model_id():
+    global LOCK, MODEL_ID
+    with LOCK:
+        MODEL_ID += 1
+        model_name = MODEL_NAMES[MODEL_ID]
+        if MODEL_ID >= len(MODEL_NAMES):
+            model_name = model_name + "_" + int(MODEL_ID/len(MODEL_NAMES))
+        return model_name
+
+def reset_user_friendly_model_id():
+    global LOCK, MODEL_ID
+    with LOCK:
+        MODEL_ID = -1
+
+
+exit_event = Event()    
+for sig in ('TERM', 'HUP', 'INT'):
+    signal.signal(getattr(signal, 'SIG'+sig), lambda: exit_event.set())
+
+
+## Experiment and Model statusses
+CREATED_STATUS = 'created'
+RUNNING_STATUS = 'running'
+FAILED_STATUS = 'failed'
+STOPPED_STATUS = 'stopped'
+COMPLETED_STATUS = 'completed'
+
+## Hyperparameter types
+HP_CHOICE = 'hp_choice'
+HP_LOGUNIFORM = 'hp_loguniform'
+HP_QLOGUNIFORM = 'hp_qloguniform'
+HP_QUNIFORM = 'hp_quniform'
+HP_UNIFORM = 'hp_uniform'
+
+## Hyperparameter dtypes
+DTYPE_STR = 'dtype_str'
+DTYPE_INT = 'dtype_int'
+DTYPE_FLOAT = 'dtype_float'
+
+## Hyperparameter search procedures
+MS_GRID_SEARCH = 'GridSearch'
+MS_RANDOM_SEARCH = 'RandomSearch'
+MS_HYPEROPT_SEARCH = 'HyperOpt'
diff --git a/cerebro/commons/model_names.txt b/cerebro/commons/model_names.txt
new file mode 100644
index 00000000..2aa1a909
--- /dev/null
+++ b/cerebro/commons/model_names.txt
@@ -0,0 +1,18239 @@
+Michael
+Christopher
+Jessica
+Matthew
+Ashley
+Jennifer
+Joshua
+Amanda
+Daniel
+David
+James
+Robert
+John
+Joseph
+Andrew
+Ryan
+Brandon
+Jason
+Justin
+Sarah
+William
+Jonathan
+Stephanie
+Brian
+Nicole
+Nicholas
+Anthony
+Heather
+Eric
+Elizabeth
+Adam
+Megan
+Melissa
+Kevin
+Steven
+Thomas
+Timothy
+Christina
+Kyle
+Rachel
+Laura
+Lauren
+Amber
+Brittany
+Danielle
+Richard
+Kimberly
+Jeffrey
+Amy
+Crystal
+Michelle
+Tiffany
+Jeremy
+Benjamin
+Mark
+Emily
+Aaron
+Charles
+Rebecca
+Jacob
+Stephen
+Patrick
+Sean
+Erin
+Zachary
+Jamie
+Kelly
+Samantha
+Nathan
+Sara
+Dustin
+Paul
+Angela
+Tyler
+Scott
+Katherine
+Andrea
+Gregory
+Erica
+Mary
+Travis
+Lisa
+Kenneth
+Bryan
+Lindsey
+Kristen
+Jose
+Alexander
+Jesse
+Katie
+Lindsay
+Shannon
+Vanessa
+Courtney
+Christine
+Alicia
+Cody
+Allison
+Bradley
+Samuel
+Shawn
+April
+Derek
+Kathryn
+Kristin
+Chad
+Jenna
+Tara
+Maria
+Krystal
+Jared
+Anna
+Edward
+Julie
+Peter
+Holly
+Marcus
+Kristina
+Natalie
+Jordan
+Victoria
+Jacqueline
+Corey
+Keith
+Monica
+Juan
+Donald
+Cassandra
+Meghan
+Joel
+Shane
+Phillip
+Patricia
+Brett
+Ronald
+Catherine
+George
+Antonio
+Cynthia
+Stacy
+Kathleen
+Raymond
+Carlos
+Brandi
+Douglas
+Nathaniel
+Ian
+Craig
+Brandy
+Alex
+Valerie
+Veronica
+Cory
+Whitney
+Gary
+Derrick
+Philip
+Luis
+Diana
+Chelsea
+Leslie
+Caitlin
+Leah
+Natasha
+Erika
+Casey
+Latoya
+Erik
+Dana
+Victor
+Brent
+Dominique
+Frank
+Brittney
+Evan
+Gabriel
+Julia
+Candice
+Karen
+Melanie
+Adrian
+Stacey
+Margaret
+Sheena
+Wesley
+Vincent
+Alexandra
+Katrina
+Bethany
+Nichole
+Larry
+Jeffery
+Curtis
+Carrie
+Todd
+Blake
+Christian
+Randy
+Dennis
+Alison
+Trevor
+Seth
+Kara
+Joanna
+Rachael
+Luke
+Felicia
+Brooke
+Austin
+Candace
+Jasmine
+Jesus
+Alan
+Susan
+Sandra
+Tracy
+Kayla
+Nancy
+Tina
+Krystle
+Russell
+Jeremiah
+Carl
+Miguel
+Tony
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+Chaneka
+Chandria
+Chancelor
+Chamroeun
+Chalsea
+Chalon
+Chalmers
+Chalea
+Chae
+Chadae
+Chablis
+Cerrissa
+Cerita
+Ceri
+Cephas
+Cena
+Celines
+Celestia
+Celes
+Ceasare
+Cayman
+Cayley
+Cayle
+Caylan
+Caycie
+Catrell
+Catoya
+Catiria
+Cathlena
+Cathi
+Catheryne
+Cathern
+Catalino
+Cassee
+Caspar
+Cas
+Carrianne
+Carrera
+Carrell
+Carolos
+Carolan
+Carola
+Carmisha
+Carmencita
+Carmalita
+Carlyne
+Carlson
+Carlisle
+Carlea
+Carissia
+Cariann
+Caree
+Caralynn
+Can
+Candyse
+Candrea
+Candias
+Candia
+Camy
+Camryn
+Cammeron
+Camia
+Camacho
+Calum
+Calogero
+Callum
+Calle
+Callahan
+Calena
+Caleen
+Calab
+Caitland
+Cailen
+Caela
+Cacia
+Bushra
+Bunny
+Bulmaro
+Bubba
+Bryttani
+Brytney
+Bryden
+Bryceson
+Bruna
+Brookes
+Brookelyn
+Bronston
+Brodi
+Brodey
+Broadus
+Brndon
+Britteney
+Britnei
+British
+Briston
+Briona
+Brin
+Briget
+Bri
+Brick
+Briar
+Briannon
+Briam
+Bresha
+Brentyn
+Brenin
+Brenee
+Brendalis
+Brenae
+Breianna
+Bre
+Breezie
+Breeze
+Breean
+Breda
+Breane
+Brandylee
+Brandun
+Brandonlee
+Brandilynn
+Brandenn
+Brandelyn
+Brandeis
+Brandalynn
+Branch
+Braiden
+Brack
+Bowman
+Borden
+Bond
+Bolivar
+Bobbye
+Bobbiesue
+Bliss
+Blase
+Blakeley
+Biridiana
+Bionca
+Billye
+Bijal
+Betti
+Betsi
+Bethlehem
+Berton
+Bernetta
+Beret
+Bentzion
+Bently
+Benjmain
+Benji
+Benjain
+Benajamin
+Belkys
+Belicia
+Bedford
+Beatrix
+Baylor
+Baudelia
+Bassem
+Basma
+Basilia
+Barri
+Barett
+Barbi
+Barbarita
+Balinda
+Azizah
+Ayumi
+Ayleen
+Ayinde
+Aydee
+Aviel
+Avian
+Averie
+Avelina
+Aunna
+Audrianna
+Audia
+Audel
+Aubrei
+Aubrea
+Atreyu
+Atlas
+Atlantis
+Atisha
+Atie
+Athenia
+Atheena
+Atanacio
+Asti
+Asleigh
+Ashlley
+Ashlely
+Ashland
+Ashea
+Ashaunti
+Aser
+Asael
+Arwyn
+Arvis
+Arvell
+Aruna
+Artrice
+Artemus
+Arrow
+Arran
+Arpan
+Arno
+Arnetra
+Arlon
+Arlina
+Arleigh
+Aristotle
+Aristotelis
+Arifa
+Arieal
+Argiro
+Arek
+Ared
+Arch
+Arabia
+Aquita
+Aquino
+Aquil
+Apryll
+Anupama
+Antwyne
+Antwion
+Antrone
+Antown
+Antowan
+Antoria
+Antonea
+Antero
+Anrew
+Anorea
+Annya
+Anniemarie
+Annett
+Annell
+Annelisa
+Anneli
+Annelies
+Anndria
+Annarose
+Annamary
+Annalynn
+Annalyn
+Annalissa
+Annaliisa
+Anjani
+Anitria
+Anis
+Anikka
+Aniesha
+Aniela
+Angelos
+Angell
+Angelino
+Angeligue
+Angelamarie
+Angelamaria
+Angalena
+Anetta
+Aneta
+Anedra
+Andru
+Andris
+Andrewjames
+Andren
+Andray
+Andice
+Ander
+Anda
+Ancil
+Anber
+Anaya
+Anastazia
+Anastassia
+Anan
+Analuisa
+Analaura
+Anahita
+Amjad
+Aminda
+Amika
+Ameshia
+Amera
+Ameisha
+Ambriel
+Ambr
+Ambreia
+Ambor
+Amatullah
+Amandah
+Amalie
+Alyss
+Alycen
+Alxis
+Alvita
+Alven
+Alsion
+Alsha
+Almon
+Almetra
+Almee
+Almando
+Allex
+Allesandra
+Allanna
+Allah
+Alka
+Alixandria
+Alister
+Alissia
+Alisah
+Alin
+Aliki
+Alie
+Aliecia
+Alfredia
+Alfonza
+Alfonse
+Alez
+Aleyda
+Alexius
+Aletheia
+Alesander
+Aleisa
+Alegra
+Aldrick
+Alder
+Akhil
+Akeim
+Akeila
+Akbar
+Ajeenah
+Ajani
+Aiysha
+Aine
+Aina
+Aiman
+Ailyn
+Aidee
+Aicia
+Ahslee
+Ahsha
+Agata
+Afshin
+Afnan
+Afaf
+Aerica
+Adwoa
+Adraine
+Adon
+Adonia
+Adnrea
+Adley
+Adine
+Adian
+Adem
+Adelbert
+Adeel
+Adama
+Adal
+Adaliz
+Adaira
+Achilles
+Abubakar
+Absalon
+Abie
+Abdullatif
+Abdulla
+Abbye
+Abbagail
+Abayomi
+Aarthi
+Aarik
+Aamil
+Aamanda
\ No newline at end of file
diff --git a/cerebro/database/models.py b/cerebro/database/models.py
deleted file mode 100644
index fe2937c4..00000000
--- a/cerebro/database/models.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2021 Supun Nakandala and Arun Kumar. 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 uuid
-from datetime import datetime
-from . import db
-
-
-class ParamDef(db.Model):
-    id = db.Column(db.String(32), primary_key=True)
-    name = db.Column(db.String(32))
-    param_type = db.Column(db.String(32))
-    values = db.Column(db.String(512))
-    min_val = db.Column(db.Float())
-    max_val = db.Column(db.Float())
-    count = db.Column(db.Integer())
-    base = db.Column(db.Integer())
-
-    def __init__(self, name, param_type, values=None, min_val=0, max_val=0, count=0, base=0):
-        self.id = str(uuid.uuid4())
-        self.name = name
-        self.param_type = param_type
-        # FIXME: Find a better way to store list of strings
-        self.values = ",".join(values)
-        self.min_val = min_val
-        self.max_val = max_val
-        self.count = count
-        self.base = base
-
-
-class ParamVal(db.Model):
-    id = db.Column(db.String(32), primary_key=True)
-    name = db.Column(db.String(32))
-    value = db.Column(db.String(32))
-    value_type = db.Column(db.String(32))
-    
-    def __init__(self, name, value, value_type):
-        self.id = str(uuid.uuid4())
-        self.name = name
-        self.value = value
-        self.value_type = value_type
-
diff --git a/cerebro/database/__init__.py b/cerebro/db/__init__.py
similarity index 100%
rename from cerebro/database/__init__.py
rename to cerebro/db/__init__.py
diff --git a/cerebro/db/dao.py b/cerebro/db/dao.py
new file mode 100644
index 00000000..3412141a
--- /dev/null
+++ b/cerebro/db/dao.py
@@ -0,0 +1,142 @@
+# Copyright 2021 Supun Nakandala and Arun Kumar. 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 uuid
+from datetime import datetime
+from . import db
+from ..commons.constants import *
+
+
+class Metric(db.Model):
+    name = db.Column(db.String(32), primary_key=True)
+    model_id = db.Column(db.String(32), db.ForeignKey('model.id'), primary_key=True)
+    values = db.Column(db.String(8192))
+    
+    def __init__(self, model_id, name, values):
+        self.model_id = model_id
+        self.name = name
+        self.values = ",".join(["{:.4f}".format(x) for x in values])
+
+    def __repr__(self):
+        return '<ParamVal %r, %r>' % self.name, self.model_id
+
+
+class Experiment(db.Model):
+    id = db.Column(db.String(32), primary_key=True)
+    name = db.Column(db.String(32))
+    description = db.Column(db.String(512))
+    creation_time = db.Column(db.DateTime)
+    last_update_time = db.Column(db.DateTime)
+    status = db.Column(db.String(32))
+    model_selection_algorithm = db.Column(db.String(32))
+    max_num_models = db.Column(db.Integer())
+    feature_columns = db.Column(db.String(512))
+    label_columns = db.Column(db.String(512))
+    max_train_epochs = db.Column(db.Integer())
+    data_store_prefix_path = db.Column(db.String(512))
+    executable_entrypoint = db.Column(db.String(512))
+    exception_message = db.Column(db.String(4096))
+
+    param_defs = db.relationship('ParamDef', backref='experiment', lazy='dynamic')
+    models = db.relationship('Model', backref='model', lazy='dynamic')
+
+
+    def __init__(self, name, description, model_selection_algorithm, max_num_models, feature_columns, label_columns, max_train_epochs,
+        data_store_prefix_path, executable_entrypoint):
+        
+        self.id = str(uuid.uuid4())
+        self.name = name
+        self.description = description
+        self.creation_time = datetime.utcnow()
+        self.last_update_time = self.creation_time
+        self.status = 'created'
+        self.model_selection_algorithm = model_selection_algorithm
+        self.max_num_models = max_num_models
+        self.feature_columns = feature_columns
+        self.label_columns = label_columns
+        self.max_train_epochs = max_train_epochs
+        self.data_store_prefix_path = data_store_prefix_path
+        self.executable_entrypoint = executable_entrypoint
+
+    def __repr__(self):
+        return '<Experiment %r>' % self.id
+
+
+class Model(db.Model):
+    id = db.Column(db.String(32), primary_key=True)
+    exp_id = db.Column(db.String(32), db.ForeignKey('experiment.id'))
+    creation_time = db.Column(db.DateTime)
+    last_update_time = db.Column(db.DateTime)
+    status = db.Column(db.String(32))
+    num_trained_epochs = db.Column(db.Integer())
+    max_train_epochs = db.Column(db.Integer())
+    warm_start_model_id = db.Column(db.String(32))
+    exception_message = db.Column(db.String(4096))
+
+    param_vals = db.relationship('ParamVal', backref='model', lazy='dynamic')
+    metrics = db.relationship('Metric', backref='model', lazy='dynamic')
+
+    def __init__(self, model_id, exp_id, num_trained_epochs, max_train_epochs, warm_start_model_id=None):
+        self.id = model_id
+        self.exp_id = exp_id        
+        self.creation_time = datetime.utcnow()
+        self.last_update_time = self.creation_time
+        self.status = 'created'
+        self.num_trained_epochs = num_trained_epochs
+        self.max_train_epochs = max_train_epochs
+        self.warm_start_model_id = warm_start_model_id
+
+    def __repr__(self):
+        return '<Model %r>' % self.id
+
+
+class ParamDef(db.Model):
+    exp_id = db.Column(db.String(32), db.ForeignKey('experiment.id'), primary_key=True)
+    name = db.Column(db.String(32), primary_key=True)
+    param_type = db.Column(db.String(32))
+    choices = db.Column(db.String(512))
+    min = db.Column(db.Float())
+    max = db.Column(db.Float())
+    q = db.Column(db.Integer())
+    dtype = db.Column(db.String(32))
+
+    def __init__(self, exp_id, name, param_type, choices=None, min=0, max=0, q=0, dtype=DTYPE_STR):
+        self.exp_id = exp_id
+        self.name = name
+        self.param_type = param_type
+        self.choices = choices
+        self.min = min
+        self.max = max
+        self.q = q
+        self.dtype = dtype
+
+
+    def __repr__(self):
+        return '<ParamDef %r, %r>' % self.exp_id, self.name
+
+
+class ParamVal(db.Model):
+    name = db.Column(db.String(32), db.ForeignKey('param_def.name'), primary_key=True)
+    model_id = db.Column(db.String(32), db.ForeignKey('model.id'), primary_key=True)
+    value = db.Column(db.String(32))
+    dtype = db.Column(db.String(32))
+    
+    def __init__(self, model_id, name, value, dtype):
+        self.model_id = model_id
+        self.name = name
+        self.value = str(value)
+        self.dtype = dtype
+
+    def __repr__(self):
+        return '<ParamVal %r, %r>' % self.name, self.model_id
diff --git a/cerebro/keras/params.py b/cerebro/keras/params.py
index f3af2e75..ac3b20e9 100644
--- a/cerebro/keras/params.py
+++ b/cerebro/keras/params.py
@@ -123,12 +123,6 @@ def setTransformationFn(self, value):
     def getTransformationFn(self):
         raise NotImplementedError('Abstract class. Method not implement!')
 
-    def setModelUpdateFn(self, value):
-        raise NotImplementedError('Abstract class. Method not implement!')
-
-    def getModelUpdateFn(self):
-        raise NotImplementedError('Abstract class. Method not implement!')
-
 
 class CerebroModelParams(object):
 
diff --git a/cerebro/keras/spark/params.py b/cerebro/keras/spark/params.py
index 788e6119..116274e5 100644
--- a/cerebro/keras/spark/params.py
+++ b/cerebro/keras/spark/params.py
@@ -72,8 +72,10 @@ class SparkEstimatorParams(Params, CerebroEstimatorParams):
     verbose = Param(Params._dummy(), 'verbose', 'verbose flag (0=silent, 1=enabled, other values used by frameworks)',
                     typeConverter=TypeConverters.toInt)
 
-    run_id = Param(Params._dummy(), 'run_id',
-                   'unique ID for this run, if run already exists, '
+    run_id = Param(Params._dummy(), 'run_id', 'unique ID for this run', typeConverter=TypeConverters.toString)
+    
+    run_name = Param(Params._dummy(), 'run_name',
+                   'unique name for this run, if checkpoint already exists for the run name, '
                    'then training will resume from last checkpoint in the store',
                    typeConverter=TypeConverters.toString)
 
@@ -243,17 +245,18 @@ def setRunId(self, value):
     def getRunId(self):
         return self.getOrDefault(self.run_id)
 
+    def setRunName(self, value):
+        return self._set(run_name=value)
+
+    def getRunName(self):
+        return self.getOrDefault(self.run_name)
+
     def setTransformationFn(self, value):
         return self._set(transformation_fn=value)
 
     def getTransformationFn(self):
         return self.getOrDefault(self.transformation_fn)
 
-    def setModelUpdateFn(self, value):
-        return self._set(model_update_fn=value)
-
-    def getModelUpdateFn(self):
-        return self.getOrDefault(self.model_update_fn)
 
 class SparkModelParams(HasOutputCols, CerebroModelParams):
     history = Param(Params._dummy(), 'history', 'history')
diff --git a/cerebro/keras/spark/util.py b/cerebro/keras/spark/util.py
index cfcd2155..4720012f 100644
--- a/cerebro/keras/spark/util.py
+++ b/cerebro/keras/spark/util.py
@@ -34,11 +34,10 @@ class TFKerasUtil(object):
 
     @staticmethod
     def fit_sub_epoch_fn():
-        def fn(starting_epoch, model, train_data, steps_per_epoch, callbacks, verbose):
+        def fn(starting_epoch, model, train_data, callbacks, verbose):
             return model.fit(
                 train_data,
                 initial_epoch=starting_epoch,
-                steps_per_epoch=steps_per_epoch,
                 callbacks=callbacks,
                 verbose=verbose,
                 epochs=starting_epoch + 1)
@@ -47,9 +46,8 @@ def fn(starting_epoch, model, train_data, steps_per_epoch, callbacks, verbose):
 
     @staticmethod
     def eval_sub_epoch_fn():
-        def fn(_, model, val_data, validation_steps, callbacks, verbose):
-            return model.evaluate(val_data, steps=validation_steps, callbacks=callbacks,
-                                  verbose=verbose)
+        def fn(_, model, val_data, callbacks, verbose):
+            return model.evaluate(val_data, callbacks=callbacks, verbose=verbose)
 
         return fn
 
@@ -57,14 +55,10 @@ def fn(_, model, val_data, validation_steps, callbacks, verbose):
     def make_dataset_fn(feature_columns, label_columns, sample_weight_col, metadata,
                         input_shapes, output_shapes, output_names, batch_size):
         # Check if any of the columns are only SparseVector
-        has_sparse_col = any(metadata[col]['is_sparse_vector_only']
-                             for col in label_columns + feature_columns)
+        has_sparse_col = any(metadata[col]['is_sparse_vector_only'] for col in label_columns + feature_columns)
 
-        reshape = TFKerasUtil._reshape_fn(
-            sample_weight_col, feature_columns, label_columns, metadata)
-        prep_data_tf_keras = _prep_data_fn(
-            has_sparse_col, sample_weight_col, feature_columns,
-            label_columns, input_shapes, output_shapes, output_names)
+        reshape = TFKerasUtil._reshape_fn(sample_weight_col, feature_columns, label_columns, metadata)
+        prep_data_tf_keras = _prep_data_fn(has_sparse_col, sample_weight_col, feature_columns, label_columns, input_shapes, output_shapes, output_names)
 
         def fn(reader, shuffle_buffer_size, shuffle=False):
             from petastorm.tf_utils import make_petastorm_dataset
@@ -77,8 +71,8 @@ def fn(reader, shuffle_buffer_size, shuffle=False):
             if has_sparse_col:
                 dataset = dataset.batch(1).map(reshape, num_parallel_calls=tf.data.experimental.AUTOTUNE)
 
-            dataset = dataset.batch(batch_size) \
-                .map(prep_data_tf_keras, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+            dataset = dataset.batch(batch_size).map(prep_data_tf_keras, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+            
             return dataset
 
         return fn
diff --git a/cerebro/storage/base.py b/cerebro/storage/base.py
index b4ccd0ae..2d790442 100644
--- a/cerebro/storage/base.py
+++ b/cerebro/storage/base.py
@@ -99,7 +99,7 @@ def sync_fn(self, run_id):
         """Returns a function that synchronises given path recursively into run path for `run_id`."""
         raise NotImplementedError()
 
-    def to_remote(self, run_id, dataset_idx):
+    def to_remote(self, run_id, dataset_idx=None):
         """Returns a view of the store that can execute in a remote environment without Horoovd deps."""
         attrs = self._remote_attrs(run_id, dataset_idx)
 
diff --git a/cerebro/tune/base.py b/cerebro/tune/base.py
index 4c110c4e..0fccf7a6 100644
--- a/cerebro/tune/base.py
+++ b/cerebro/tune/base.py
@@ -22,7 +22,6 @@
 from ..commons.util import fix_huggingface_layer_methods_and_add_to_custom_objects
 from ..backend import constants
 
-
 class _HP(object):
     def sample_value(self):
         """ randomly samples a value"""
@@ -193,19 +192,20 @@ def fit(self, df):
         if self.verbose >= 1: print(
             'CEREBRO => Time: {}, Initializing Data Loaders'.format(
                 datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
-        self.backend.initialize_data_loaders(self.store, None, self.feature_cols + self.label_cols)
+        self.backend.initialize_data_loaders(self.store, self.feature_cols + self.label_cols)
 
         try:
             if self.verbose >= 1: print('CEREBRO => Time: {}, Launching Model Selection Workload'.format(
                 datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
-            result = self._fit_on_prepared_data(None, metadata)
+            result = self._fit_on_prepared_data(metadata)
             return result
         finally:
             # teardown the backend workers
             if self.verbose >= 1: print(
                 'CEREBRO => Time: {}, Terminating Workers'.format(
                     datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
-            self.backend.teardown_workers()
+            if self.backend is not None:
+                self.backend.teardown_workers()
 
     def fit_on_prepared_data(self):
         """
@@ -214,7 +214,6 @@ def fit_on_prepared_data(self):
         :return: cerebro.tune.ModelSelectionResult
         """
         _, _, metadata, _ = self.backend.get_metadata_from_parquet(self.store, self.label_cols, self.feature_cols)
-        dataset_index=None
 
         # initialize backend and data loaders
         if self.verbose >= 1: print(
@@ -224,63 +223,34 @@ def fit_on_prepared_data(self):
         if self.verbose >= 1: print(
             'CEREBRO => Time: {}, Initializing Data Loaders'.format(
                 datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
-        self.backend.initialize_data_loaders(self.store, dataset_index, self.feature_cols + self.label_cols)
+        self.backend.initialize_data_loaders(self.store, self.feature_cols + self.label_cols)
 
         try:
             if self.verbose >= 1: print('CEREBRO => Time: {}, Launching Model Selection Workload'.format(
                 datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
-            result = self._fit_on_prepared_data(dataset_index, metadata)
+            result = self._fit_on_prepared_data(metadata)
             return result
         finally:
             # teardown the backend workers
             if self.verbose >= 1: print(
                 'CEREBRO => Time: {}, Terminating Workers'.format(
                     datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
-            self.backend.teardown_workers()
+            if self.backend is not None:
+                self.backend.teardown_workers()
 
     def _fit_on_prepared_data(self):
         raise NotImplementedError('method not implemented')
 
     def _estimator_gen_fn_wrapper(self, params):
-        # Disable GPUs when building the model to prevent memory leaks
-        if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'):
-            # See https://github.com/tensorflow/tensorflow/issues/33168
-            os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-        else:
-            tf.keras.set_session(tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})))
-
-        tf.keras.backend.clear_session()
-        est = self.estimator_gen_fn(params)
-        est.setHyperParams(params)
-        est.setFeatureCols(self.feature_cols)
-        est.setLabelCols(self.label_cols)
-        est.setStore(self.store)
-        est.setVerbose(self.verbose)
-
-        # Workaround for the issue with huggingface layers needing a python
-        # object as config (not a dict) and explicit definition of get_config method.
-        # We monkey patch the __init__ method get_config methods of such layers.
-        fix_huggingface_layer_methods_and_add_to_custom_objects(est)
-
-        return est
+        return estimator_gen_fn_wrapper(self.estimator_gen_fn, params, self.feature_cols, self.label_cols, self.store, self.verbose)
 
     def _log_epoch_metrics_to_tensorboard(self, estimators, estimator_results):
         # logging to TensorBoard
-        for est in estimators:
-            remote_store = self.store.to_remote(est.getRunId(), None)
-            with remote_store.get_local_output_dir() as logs_dir:
-                log_model_epoch_metrics(est.getRunId(), logs_dir,
-                                        estimator_results[est.getRunId()],
-                                        est.getEpochs(), self.verbose)
-            remote_store.sync(logs_dir)
+        log_epoch_metrics_to_tensorboard(estimators, estimator_results, store, verbose)
 
     def _log_hp_to_tensorboard(self, estimators, hparams):
         # logging to TensorBoard
-        for i, est in enumerate(estimators):
-            remote_store = self.store.to_remote(est.getRunId(), None)
-            with remote_store.get_local_output_dir() as logs_dir:
-                log_model_hps(logs_dir, est.getRunId(), hparams[i], self.verbose)
-            remote_store.sync(logs_dir)
+        log_hp_to_tensorboard(estimators, hparams, self.store, self.verbose)
 
 
 class ModelSelectionResult(object):
@@ -380,7 +350,7 @@ def log_model_epoch_metrics(model_id, logdir, metrics, step_number, verbose=1):
     :param logdir:
     :param metrics:
     :param step_number:
-    :param verbose:
+    :param verbose:log_epoch_metrics_to_tensorboard(estimators, estimator_results, store, verbose)
     """
     with tf.summary.create_file_writer(logdir).as_default():
         for key in metrics:
@@ -434,3 +404,66 @@ def update_model_results(estimator_results, epoch_results):
                 estimator_results[model_id][k].append(res[k])
             else:
                 estimator_results[model_id][k] = [res[k]]
+
+def log_hp_to_tensorboard(estimators, hparams, store, verbose):
+    """
+    Helper method to log hparams to the TensorBoard
+    :param estimators:
+    :param hparams:
+    :param store: A single store common for all estimators or a dictionary containing stores for each estimator indexed by model id.
+    :param verbose:
+    """
+    for i, est in enumerate(estimators):
+        if type(store) == dict:
+            a_store = store[est.getRunId()]
+        else:
+            a_store = store
+        remote_store = a_store.to_remote(est.getRunId(), None)
+        with remote_store.get_local_output_dir() as logs_dir:
+            log_model_hps(logs_dir, est.getRunId(), hparams[i], verbose)
+        remote_store.sync(logs_dir)
+
+def log_epoch_metrics_to_tensorboard(estimators, estimator_results, store, verbose):
+    """
+    Helper method to log hparams to the TensorBoard
+    :param estimators:
+    :param estimator_results:
+    :param store: A single store common for all estimators or a dictionary containing stores for each estimator indexed by model id.
+    :param verbose:
+    """
+    for est in estimators:
+        if type(store) == dict:
+            a_store = store[est.getRunId()]
+        else:
+            a_store = store
+        remote_store = a_store.to_remote(est.getRunId(), None)
+        with remote_store.get_local_output_dir() as logs_dir:
+            log_model_epoch_metrics(est.getRunId(), logs_dir, estimator_results[est.getRunId()], est.getEpochs(), verbose)
+        remote_store.sync(logs_dir)
+
+
+def estimator_gen_fn_wrapper(estimator_gen_fn, params, feature_cols, label_cols, store, verbose):
+    """
+    Function wrapping a user provided estimator gen function.
+    """
+    # Disable GPUs when building the model to prevent memory leaks
+    if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'):
+        # See https://github.com/tensorflow/tensorflow/issues/33168
+        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+    else:
+        tf.keras.set_session(tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})))
+
+    tf.keras.backend.clear_session()
+    est = estimator_gen_fn(params)
+    est.setHyperParams(params)
+    est.setFeatureCols(feature_cols)
+    est.setLabelCols(label_cols)
+    est.setStore(store)
+    est.setVerbose(verbose)
+
+    # Workaround for the issue with huggingface layers needing a python
+    # object as config (not a dict) and explicit definition of get_config method.
+    # We monkey patch the __init__ method get_config methods of such layers.
+    fix_huggingface_layer_methods_and_add_to_custom_objects(est)
+
+    return est
\ No newline at end of file
diff --git a/cerebro/tune/daemon.py b/cerebro/tune/daemon.py
new file mode 100644
index 00000000..c808f888
--- /dev/null
+++ b/cerebro/tune/daemon.py
@@ -0,0 +1,159 @@
+# Copyright 2020 Supun Nakandala, Yuhao Zhang, and Arun Kumar. 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 os
+import time
+import logging
+import traceback
+import tensorflow as tf
+from .base import log_hp_to_tensorboard, log_epoch_metrics_to_tensorboard, estimator_gen_fn_wrapper, update_model_results
+from ..commons.constants import *
+from ..db.dao import *
+from ..storage import LocalStore, HDFSStore
+from importlib import import_module
+from sqlalchemy import and_
+
+
+def sub_epoch_scheduler(app, db, backend, inter_epoch_wait_time=5, verbose=True):
+    """
+    Sub-epoch scheduling daemon. Reads trainable model configs from the database and runs them on the provided backend.
+    :param app: Flask applicarion.
+    :param db: SQLAlchemy DB object.
+    :param backend: Cerebro backend object
+    :param inter_epoch_wait_time:
+    :param verbose:
+    """
+
+    with app.app_context():
+        while not exit_event.is_set():
+            all_models = all_models = Model.query.filter(and_(Model.status.in_([CREATED_STATUS, RUNNING_STATUS]), Model.max_train_epochs > Model.num_trained_epochs)).all()
+            if all_models is not None and len(all_models) > 0:
+                estimators = []
+                estimator_results = {}
+                all_stores = {}
+                all_labels = {}
+                all_features = {}
+                for m in all_models:
+                    try:
+                        exp_obj = Experiment.query.filter(Experiment.id == m.exp_id).one()
+                        data_store_prefix_path = exp_obj.data_store_prefix_path
+
+                        if data_store_prefix_path.startswith('hdfs://'):
+                            store = HDFSStore(prefix_path=data_store_prefix_path)
+                        else:
+                            store = LocalStore(prefix_path=data_store_prefix_path)
+                    
+                        param = {}
+                        for d in m.param_vals:
+                            if d.dtype == DTYPE_FLOAT:
+                                param[d.name] = float(d.value)
+                            elif d.dtype == DTYPE_INT:
+                                param[d.name] = int(d.value)
+                            else:
+                                param[d.name] = d.value
+
+                        mod, f = exp_obj.executable_entrypoint.split(':')
+                        mod = import_module(mod)
+                        estimator_gen_fn = getattr(mod, f)
+                        
+                        features, labels = exp_obj.feature_columns.split(','), exp_obj.label_columns.split(',')
+                        est = estimator_gen_fn_wrapper(estimator_gen_fn, param, features, labels, store, verbose)
+                        est.setRunId(m.id)
+                        est.setEpochs(m.num_trained_epochs)
+
+                        # Creating model checkpoint
+                        remote_store = store.to_remote(est.getRunId())
+                        with remote_store.get_local_output_dir() as run_output_dir:
+                            tf.compat.v1.reset_default_graph
+                            model = est._compile_model(est._get_keras_utils())
+
+                            if m.warm_start_model_id is not None and not est._has_checkpoint(m.id):
+                                # https://www.tensorflow.org/guide/keras/save_and_serialize#apis_for_in-memory_weight_transfer
+                                remote_store2 = store.to_remote(m.warm_start_model_id)
+                                with remote_store2.get_local_output_dir() as run_output_dir2:
+                                    model2 = est._compile_model(est._get_keras_utils())
+                                    model.set_weights(model2.get_weights())
+
+                                warm_start_model = Model.query.filter(Model.id == m.warm_start_model_id).one()
+                                db.session.refresh(m)
+                                m.num_trained_epochs = warm_start_model.num_trained_epochs
+                                db.session.commit()
+
+                                est.setEpochs(m.num_trained_epochs)
+
+                                for metric in warm_start_model.metrics:
+                                    new_metric = Metric(m.id, metric.name, [float(x) for x in metric.values.split(",")])
+                                    db.session.add(new_metric)
+                                    db.session.commit()
+
+                            ckpt_file = os.path.join(run_output_dir, remote_store.checkpoint_filename)
+                            model.save(ckpt_file)
+                            remote_store.sync(run_output_dir)
+                            tf.compat.v1.reset_default_graph
+
+                        estimators.append(est)
+                        all_stores[est.getRunId()] = store
+                        all_features[est.getRunId()] = features
+                        all_labels[est.getRunId()] = labels
+                        
+                        if m.status == CREATED_STATUS:
+                            db.session.refresh(m)
+                            m.status = RUNNING_STATUS
+                            db.session.commit()
+                            # Log hyperparameters to TensorBoard
+                            log_hp_to_tensorboard([est], [param], store, verbose)
+
+                        estimator_results[m.id] = {}
+                        for metric in m.metrics:
+                            estimator_results[m.id][metric.name] = [float(x) for x in metric.values.split(',')]
+                    except Exception as e:
+                        logging.error(traceback.format_exc())
+                        db.session.refresh(m)
+                        m.status = FAILED_STATUS
+                        m.exception_message = str(traceback.format_exc())
+                        db.session.commit()
+
+                # Trains all the models for one epoch. Also performs validation
+                epoch_results = backend.train_for_one_epoch(estimators, all_stores, all_features, all_labels)
+                update_model_results(estimator_results, epoch_results)
+
+                epoch_results = backend.train_for_one_epoch(estimators, all_stores, all_features, all_labels, is_train=False)
+                update_model_results(estimator_results, epoch_results)
+
+                log_epoch_metrics_to_tensorboard(estimators, estimator_results, all_stores, verbose)
+
+                for m in all_models:
+                    est_results = estimator_results[m.id]
+                    # Refresh to sync any model stop requests from the db
+                    db.session.refresh(m)
+                    metrics = m.metrics.all()
+                    if len(metrics) == 0:
+                        for k in est_results:
+                            db.session.add(Metric(m.id, k, est_results[k]))
+                    else:
+                        for k in est_results:
+                            metric = [metric for metric in metrics if metric.name == k][0]
+                            metric.values = ",".join(["{:.4f}".format(x) for x in est_results[k]])
+                    db.session.commit()
+
+                for m in all_models:
+                    # Refresh to sync any model stop requests from the db
+                    db.session.refresh(m)
+                    m.num_trained_epochs += 1
+                    if m.num_trained_epochs >= m.max_train_epochs:
+                        m.status = COMPLETED_STATUS
+                    db.session.commit()
+
+            # inter-epoch waiting
+            exit_event.wait(inter_epoch_wait_time)
diff --git a/cerebro/tune/grid.py b/cerebro/tune/grid.py
index 2710c525..5b3c0533 100644
--- a/cerebro/tune/grid.py
+++ b/cerebro/tune/grid.py
@@ -12,12 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
+import os
 import itertools
-
+import datetime
+import tensorflow as tf
+from sqlalchemy import and_
 import numpy as np
+import logging
+import traceback
+from ..commons.constants import *
 
 from .base import ModelSelection, is_larger_better, ModelSelectionResult, _HP, _HPChoice, update_model_results
+from ..db.dao import Model, Metric, ParamVal, ParamDef, Experiment
+from ..commons.constants import CREATED_STATUS, RUNNING_STATUS, COMPLETED_STATUS
 
 
 class GridSearch(ModelSelection):
@@ -75,8 +82,40 @@ def _to_key_value_pairs(keys, values):
         return [dict(_to_key_value_pairs(keys, prod)) for prod in itertools.product(*[v if isinstance(v, list) else \
                                                                                           v() for v in grid_values])]
 
-    def _fit_on_prepared_data(self, dataset_idx, metadata):
-        return _fit_on_prepared_data(self, dataset_idx, metadata)
+    def _fit_on_prepared_data(self, metadata):
+        return _fit_on_prepared_data(self, metadata)
+
+
+class HILGridSearch(GridSearch):
+    """Performs intermittent HIL grid search using the given param grid
+    :param exp_id: Experiment ID.
+    :param backend: Cerebro backend object (e.g., SparkBackend).
+    :param store: Cerebro store object (e.g., LocalStore, HDFSStore).
+    :param estimator_gen_fn: A function which takes 
+     in a dictionary of parameters and returns a Cerebro Estimator (e.g., cerebro.SparkEstimator).
+    :param search_space: A dictionary object defining the parameter search space.
+    :param num_epochs: Number of maximum epochs each model should be trained for.
+    :param db: SQLAlchemy DB object.
+    :param label_columns: (Optional) A list containing the names of the label/output columns (default ['label']).
+    :param feature_columns: (Optional) A list containing the names of the feature columns (default ['features']).
+    :param verbose: Debug output verbosity (0-2). Defaults to 1.
+    """
+
+    def __init__(self, exp_id, backend, store, estimator_gen_fn, search_space, num_epochs, db,
+                 label_columns=['label'], feature_columns=['features'], verbose=1):
+        super(HILGridSearch, self).__init__(backend=backend, store=store, estimator_gen_fn=estimator_gen_fn, search_space=search_space,
+            num_epochs=num_epochs, label_columns=label_columns, feature_columns=feature_columns, verbose=verbose)
+        self.exp_id = exp_id
+        self.db = db
+
+    def fit(self, df):
+        raise NotImplementedError('method not implemented')
+
+    def fit_on_prepared_data(self):
+        """
+         Execute the model selection/AutoML workload on already prepared data.
+        """
+        _hil_fit_on_prepared_data(self)
 
 
 class RandomSearch(ModelSelection):
@@ -87,6 +126,7 @@ class RandomSearch(ModelSelection):
     :param estimator_gen_fn: A function which takes
      in a dictionary of parameters and returns a Cerebro Estimator (e.g., cerebro.SparkEstimator).
     :param search_space: A dictionary object defining the parameter search space.
+    :param num_models: Maximum number of models to be explored.
     :param num_epochs: Number of maximum epochs each model should be trained for.
     :param evaluation_metric: Evaluation metric used to pick the best model (default: "loss").
     :param validation: (Optional) The ratio of the validation set (default: 0.25) or a string defining the column name
@@ -133,11 +173,46 @@ def _generate_all_param_maps(self):
             params.append(param_dict)
         return params
 
-    def _fit_on_prepared_data(self, dataset_idx, metadata):
-        return _fit_on_prepared_data(self, dataset_idx, metadata)
+    def _fit_on_prepared_data(self, metadata):
+        return _fit_on_prepared_data(self, metadata)
 
 
-def _fit_on_prepared_data(self, dataset_idx, metadata):
+class HILRandomSearch(RandomSearch):
+    """Performs intermittent HIL random search using the given param grid
+    :param exp_id: Experiment ID.
+    :param backend: Cerebro backend object (e.g., SparkBackend).
+    :param store: Cerebro store object (e.g., LocalStore, HDFSStore).
+    :param estimator_gen_fn: A function which takes 
+     in a dictionary of parameters and returns a Cerebro Estimator (e.g., cerebro.SparkEstimator).
+    :param search_space: A dictionary object defining the parameter search space.
+    :param num_models: Maximum number of models to be explored.
+    :param num_epochs: Number of maximum epochs each model should be trained for.
+    :param db: SQLAlchemy DB object.
+    :param label_columns: (Optional) A list containing the names of the label/output columns (default ['label']).
+    :param feature_columns: (Optional) A list containing the names of the feature columns (default ['features']).
+    :param verbose: Debug output verbosity (0-2). Defaults to 1.
+
+    """
+
+    def __init__(self, exp_id, backend, store, estimator_gen_fn, search_space, num_models, num_epochs, db,
+                 label_columns=['label'], feature_columns=['features'], verbose=1):
+        super(HILRandomSearch, self).__init__(backend=backend, store=store, estimator_gen_fn=estimator_gen_fn, search_space=search_space,
+            num_models=num_models, num_epochs=num_epochs, label_columns=label_columns, feature_columns=feature_columns, verbose=verbose)
+        self.exp_id = exp_id
+        self.db = db
+
+    def fit(self, df):
+        raise NotImplementedError('method not implemented')
+
+    def fit_on_prepared_data(self):
+        """
+         Execute the model selection/AutoML workload on already prepared data.
+        """
+        _hil_fit_on_prepared_data(self)
+
+
+# Batch implementation (i.e., without any user interaction) of model selection.
+def _fit_on_prepared_data(self, metadata):
     # create estimators
     estimators = [self._estimator_gen_fn_wrapper(param) for param in self.estimator_param_maps]
     estimator_results = {model.getRunId(): {} for model in estimators}
@@ -147,11 +222,11 @@ def _fit_on_prepared_data(self, dataset_idx, metadata):
 
     # Trains the models up to the number of epochs specified. For each iteration also performs validation
     for epoch in range(self.num_epochs):
-        epoch_results = self.backend.train_for_one_epoch(estimators, self.store, dataset_idx, self.feature_cols,
+        epoch_results = self.backend.train_for_one_epoch(estimators, self.store, self.feature_cols,
                                                          self.label_cols)
         update_model_results(estimator_results, epoch_results)
 
-        epoch_results = self.backend.train_for_one_epoch(estimators, self.store, dataset_idx, self.feature_cols,
+        epoch_results = self.backend.train_for_one_epoch(estimators, self.store, self.feature_cols,
                                                          self.label_cols, is_train=False)
         update_model_results(estimator_results, epoch_results)
 
@@ -164,3 +239,36 @@ def _fit_on_prepared_data(self, dataset_idx, metadata):
     best_model = models[best_model_idx]
 
     return ModelSelectionResult(best_model, estimator_results, models, [x+'__output' for x in self.label_cols])
+
+
+# Human-in-the-loop implementation
+def _hil_fit_on_prepared_data(self):
+    _, _, metadata, _ = self.backend.get_metadata_from_parquet(self.store, self.label_cols, self.feature_cols)
+
+    if self.verbose >= 1: print(
+        'CEREBRO => Time: {}, Initializing Data Loaders'.format(
+            datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
+    self.backend.initialize_data_loaders(self.store, self.feature_cols + self.label_cols)
+
+    if self.verbose >= 1: print('CEREBRO => Time: {}, Launching Model Selection Workload'.format(
+        datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
+
+    exp_id = self.exp_id
+    exp_obj = Experiment.query.filter(Experiment.id == exp_id).one()
+    db = self.db
+
+    # Creating the intial model specs.
+    param_maps = self.estimator_param_maps
+    for param_map in param_maps:
+        model_id = next_user_friendly_model_id()
+        model_dao = Model(model_id, exp_obj.id, 0, int(exp_obj.max_train_epochs))
+        db.session.add(model_dao)
+
+        for k in param_map:
+            dtype = ParamDef.query.filter(and_(ParamDef.exp_id == exp_id, ParamDef.name == k)).one().dtype
+            pval_dao = ParamVal(model_id, k, param_map[k], dtype)
+            db.session.add(pval_dao)
+            db.session.add(model_dao)
+    db.session.commit()
+    exp_obj.status = RUNNING_STATUS
+    db.session.commit()
diff --git a/cerebro/tune/tpe.py b/cerebro/tune/tpe.py
index 4767d2b2..39945d3b 100644
--- a/cerebro/tune/tpe.py
+++ b/cerebro/tune/tpe.py
@@ -91,7 +91,7 @@ def __init__(self, backend, store, estimator_gen_fn, search_space, num_models, n
         self.num_params = num_models
         self.num_epochs = num_epochs
 
-    def _fit_on_prepared_data(self, dataset_idx, metadata):
+    def _fit_on_prepared_data(self, metadata):
         trials = Trials()
         domain = Domain(None, self.hyperopt_search_space)
         rand = np.random.RandomState(constants.RANDOM_SEED)
@@ -131,11 +131,11 @@ def _fit_on_prepared_data(self, dataset_idx, metadata):
 
             # Trains the models up to the number of epochs specified. For each iteration also performs validation
             for epoch in range(self.num_epochs):
-                epoch_results = self.backend.train_for_one_epoch(estimators, self.store, dataset_idx, self.feature_cols,
+                epoch_results = self.backend.train_for_one_epoch(estimators, self.store, self.feature_cols,
                                                                  self.label_cols)
                 update_model_results(estimator_results, epoch_results)
 
-                epoch_results = self.backend.train_for_one_epoch(estimators, self.store, dataset_idx, self.feature_cols,
+                epoch_results = self.backend.train_for_one_epoch(estimators, self.store, self.feature_cols,
                                                                  self.label_cols, is_train=False)
                 update_model_results(estimator_results, epoch_results)
 
diff --git a/setup.py b/setup.py
index 3eec2f37..921973d8 100644
--- a/setup.py
+++ b/setup.py
@@ -51,7 +51,7 @@
     },
     entry_points={
         'console_scripts': [
-            'cerebro = cerebro.cerebro_server:main',
+            'cerebro = cerebro.api.cerebro_server:main',
         ],
     },
     classifiers=[


Test patch:
diff --git a/tests/model_gen_script.py b/tests/model_gen_script.py
new file mode 100644
index 00000000..a39d97ea
--- /dev/null
+++ b/tests/model_gen_script.py
@@ -0,0 +1,21 @@
+import tensorflow as tf
+from cerebro.keras import SparkEstimator
+
+def estimator_gen_fn(params):
+    model = tf.keras.models.Sequential()
+
+    model.add(tf.keras.layers.Dense(100, input_dim=692))
+    model.add(tf.keras.layers.Dense(1, input_dim=100))
+    model.add(tf.keras.layers.Activation('sigmoid'))
+
+    optimizer = tf.keras.optimizers.Adam(lr=params['learning_rate'])
+    loss = 'binary_crossentropy'
+
+    keras_estimator = SparkEstimator(
+        model=model,
+        optimizer=optimizer,
+        loss=loss,
+        metrics=['acc'],
+        batch_size=params['batch_size'])
+
+    return keras_estimator