[API] Fix pipelines creation breakage (mlrun#1679)

mlrun/api/crud/pipelines.py

@@ -23,13 +23,15 @@ def list_pipelines(
         self,
         db_session: sqlalchemy.orm.Session,
         project: str,
-        namespace: str = mlrun.mlconf.namespace,
+        namespace: typing.Optional[str] = None,
         sort_by: str = "",
         page_token: str = "",
         filter_: str = "",
         format_: mlrun.api.schemas.PipelinesFormat = mlrun.api.schemas.PipelinesFormat.metadata_only,
         page_size: typing.Optional[int] = None,
     ) -> typing.Tuple[int, typing.Optional[int], typing.List[dict]]:
+        if namespace is None:
+            namespace = mlrun.mlconf.namespace
         if project != "*" and (page_token or page_size or sort_by or filter_):
             raise mlrun.errors.MLRunInvalidArgumentError(
                 "Filtering by project can not be used together with pagination, sorting, or custom filter"
@@ -41,7 +43,12 @@ def list_pipelines(
             raise mlrun.errors.MLRunInvalidArgumentError(
                 "Summary format is not supported for list pipelines, use get instead"
             )
-        kfp_client = kfp.Client(namespace=namespace)
+        # When instead of host we provided namespace we tackled this issue
+        # https://github.com/canonical/bundle-kubeflow/issues/412
+        # TODO: When we'll move to kfp 1.4.0 (server side) it should be resolved
+        kfp_client = kfp.Client(
+            host=f"http://ml-pipeline.{namespace}.svc.cluster.local:8888"
+        )
         if project != "*":
             run_dicts = []
             while page_token is not None:
@@ -79,10 +86,17 @@ def get_pipeline(
         db_session: sqlalchemy.orm.Session,
         run_id: str,
         project: typing.Optional[str] = None,
-        namespace: str = mlrun.mlconf.namespace,
+        namespace: typing.Optional[str] = None,
         format_: mlrun.api.schemas.PipelinesFormat = mlrun.api.schemas.PipelinesFormat.summary,
     ):
-        kfp_client = kfp.Client(namespace=namespace)
+        if namespace is None:
+            namespace = mlrun.mlconf.namespace
+        # When instead of host we provided namespace we tackled this issue
+        # https://github.com/canonical/bundle-kubeflow/issues/412
+        # TODO: When we'll move to kfp 1.4.0 (server side) it should be resolved
+        kfp_client = kfp.Client(
+            host=f"http://ml-pipeline.{namespace}.svc.cluster.local:8888"
+        )
         run = None
         try:
             api_run_detail = kfp_client.get_run(run_id)
@@ -112,8 +126,10 @@ def create_pipeline(
         content_type: str,
         data: bytes,
         arguments: dict = None,
-        namespace: str = mlrun.mlconf.namespace,
+        namespace: typing.Optional[str] = None,
     ):
+        if namespace is None:
+            namespace = mlrun.mlconf.namespace
         if arguments is None:
             arguments = {}
         if "/yaml" in content_type:
@@ -141,7 +157,12 @@ def create_pipeline(
         )
 
         try:
-            kfp_client = kfp.Client(namespace=namespace)
+            # When instead of host we provided namespace we tackled this issue
+            # https://github.com/canonical/bundle-kubeflow/issues/412
+            # TODO: When we'll move to kfp 1.4.0 (server side) it should be resolved
+            kfp_client = kfp.Client(
+                host=f"http://ml-pipeline.{namespace}.svc.cluster.local:8888"
+            )
             experiment = kfp_client.create_experiment(name=experiment_name)
             run = kfp_client.run_pipeline(
                 experiment.id, run_name, pipeline_file.name, params=arguments