In reply toundefined

In reply to@_slack_kubeflow_U02HTJ9GLKT:matrix.org
Hi, I have installed Kubeflow 1.4 from manifests and I tried to deploy the sklearn-iris predictor sample and I am getting the following error: Any ideas on a way forward? Many thanks 🙏

predictor_2 = V1beta1PredictorSpec(tensorflow=V1beta1TFServingSpec(storage_uri=storage_uri,
                                                                         image=image),
                                        min_replicas=1,
                                        max_replicas=3,
                                        canary_traffic_percent=50,
                                       )

isvc_2 = V1beta1InferenceService(api_version="serving.kubeflow.org/v1beta1",
                          kind=constants.KFSERVING_KIND,
                          metadata=client.V1ObjectMeta(name=predictor_name, namespace=namespace),
                          spec=V1beta1InferenceServiceSpec(predictor=predictor_2))

kfs.replace(name=predictor_name, inferenceservice=isvc_2, namespace=namespace)

(base) jovyan@tensorboard-normal-0:~$ k get InferenceService
NAME     URL                                    READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION              AGE
demo   http://demo.test-ns.example.com          True           100                              demo-predictor-default-5sn57   8m59s

In reply to@_slack_kubeflow_U0253V66WCT:matrix.org
By the way, if I use different storage_uri , it will replace or patch successfully, but we are using TFX. Pusher, a component of TFX, saves trained-model under a folder, e.g., ./models/1, /models/2, … we need to specify the storage_uri to the ./models level for loading saved model of TF. In this case, kfserving won’t do replace or patch. Does any possible way to deal with it?

In reply to@_slack_kubeflow_UFVUV2UFP:matrix.org
Yulong that's by design, if the storage uri is not changed then nothing is deployed as we want to make storage immutable. But I think in this case you can add an annotation to indicate the version change then InferenceService will redeploy and load the correct version

In reply to@_slack_kubeflow_UFVUV2UFP:matrix.org
Any change should be surfaced on inference service yaml which is the whole point of declarative deployment