[Feature Store] Make verbose configurable (#1109)

(cherry picked from commit ffec067)

mlrun/feature_store/api.py

@@ -337,6 +337,7 @@ def preview(
     timestamp_key=None,
     namespace=None,
     options: InferOptions = None,
+    verbose=False,
 ) -> pd.DataFrame:
     """run the ingestion pipeline with local DataFrame/file data and infer features schema and stats
 
@@ -358,6 +359,7 @@ def preview(
     :param timestamp_key:  timestamp column name
     :param namespace:      namespace or module containing graph classes
     :param options:        schema and stats infer options (:py:class:`~mlrun.feature_store.InferOptions`)
+    :param verbose:        verbose log
     """
     options = options if options is not None else InferOptions.default()
     if timestamp_key is not None:
@@ -384,7 +386,9 @@ def preview(
                 entity_columns,
                 InferOptions.get_common_options(options, InferOptions.Entities),
             )
-        source = init_featureset_graph(source, featureset, namespace, return_df=True)
+        source = init_featureset_graph(
+            source, featureset, namespace, return_df=True, verbose=verbose
+        )
 
     df = infer_from_static_df(source, featureset, entity_columns, options)
     return df
@@ -415,6 +419,7 @@ def deploy_ingestion_service(
     targets: List[DataTargetBase] = None,
     name: str = None,
     run_config: RunConfig = None,
+    verbose=False,
 ):
     """Start real-time ingestion service using nuclio function
 
@@ -433,6 +438,7 @@ def deploy_ingestion_service(
     :param targets:       list of data target objects
     :param name:          name name for the job/function
     :param run_config:    service runtime configuration (function object/uri, resources, etc..)
+    :param verbose:       verbose log
     """
     if isinstance(featureset, str):
         featureset = get_feature_set_by_uri(featureset)
@@ -465,7 +471,7 @@ def deploy_ingestion_service(
     function.spec.graph_initializer = (
         "mlrun.feature_store.ingestion.featureset_initializer"
     )
-    function.verbose = True
+    function.verbose = function.verbose or verbose
     if run_config.local:
         return function.to_mock_server(namespace=get_caller_globals())
     return function.deploy()

mlrun/feature_store/common.py

@@ -67,7 +67,7 @@ def get_feature_set_by_uri(uri, project=None):
     default_project = project or config.default_project
 
     # parse store://.. uri
-    if mlrun.datastore.is_store_uri():
+    if mlrun.datastore.is_store_uri(uri):
         prefix, new_uri = mlrun.datastore.parse_store_uri(uri)
         if prefix != StorePrefix.FeatureSet:
             raise mlrun.errors.MLRunInvalidArgumentError(
@@ -85,7 +85,7 @@ def get_feature_vector_by_uri(uri, project=None):
     default_project = project or config.default_project
 
     # parse store://.. uri
-    if mlrun.datastore.is_store_uri():
+    if mlrun.datastore.is_store_uri(uri):
         prefix, new_uri = mlrun.datastore.parse_store_uri(uri)
         if prefix != StorePrefix.FeatureVector:
             raise mlrun.errors.MLRunInvalidArgumentError(

mlrun/feature_store/ingestion.py

@@ -32,7 +32,7 @@
 
 
 def init_featureset_graph(
-    source, featureset, namespace, targets=None, return_df=True,
+    source, featureset, namespace, targets=None, return_df=True, verbose=False
 ):
     """create storey ingestion graph/DAG from feature set object"""
 
@@ -41,7 +41,7 @@ def init_featureset_graph(
 
     # init targets (and table)
     targets = targets or []
-    server = create_graph_server(graph=graph, parameters={}, verbose=True)
+    server = create_graph_server(graph=graph, parameters={}, verbose=verbose)
     server.init_states(context=None, namespace=namespace, resource_cache=cache)
 
     if graph.engine != "sync":
@@ -71,7 +71,8 @@ def init_featureset_graph(
         target = get_target_driver(target, featureset)
         size = target.write_dataframe(data)
         target_status = target.update_resource_status("ready", size=size)
-        logger.info(f"wrote target: {target_status}")
+        if verbose:
+            logger.info(f"wrote target: {target_status}")
 
     return data