[Question] How to setup a runtime configuration?

Could not find any specific doc on what value to put under Kubeflow Pipelines API Endpoint when defining a Runtime Configuration.

So far I have kubeflow running on eks (aws).

What I’m trying to accomplish is a simple elyra’s pipeline executed on kubeflow pipelines.

However I cannot figure out what value to put on Kubeflow Pipelines API Endpoint.

Further info

• Elyra’s image used for jupyter server configuration: elyra/kf-notebook:3.9.0

What i’ve tried so far

Getting the ip of ml-pipeline-ui service in k8s and setting Kubeflow Pipelines API Endpoint with it.

Sorry if I missed some obvious steps…

[augustovictor]

@ptitzler I got part of it to work

First Applied PodDefault; Then criated a new jupyterlab server and selected Allow access to kubeflow pipelines in the Configurations section. When it started I created a pipeline runtime config with the following values:

Then got this message:

The logs in my notebook pod (k -n kubeflow-user-example-com logs victor-elyra-0 -f) says:

ERROR:root:Failed to get healthz info attempt 5 of 5.
Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/kfp/_client.py", line 411, in get_kfp_healthz
    response = self._healthz_api.get_healthz()
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/api/healthz_service_api.py", line 63, in get_healthz
    return self.get_healthz_with_http_info(**kwargs)  # noqa: E501
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/api/healthz_service_api.py", line 134, in get_healthz_with_http_info
    return self.api_client.call_api(
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
    return self.__call_api(resource_path, method,
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
    raise e
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
    response_data = self.request(
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
    return self.rest_client.GET(url,
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
    return self.request("GET", url,
  File "/opt/conda/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
    raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (403)
Reason: Forbidden
HTTP response headers: HTTPHeaderDict({'content-length': '19', 'content-type': 'text/plain', 'date': 'Tue, 18 Oct 2022 11:09:29 GMT', 'server': 'envoy', 'x-envoy-upstream-service-time': '1'})
HTTP response body: RBAC: access denied

---

Then i tried adding a roleBinding between the ServiceAccount default-editor under my user’s namespace, and ml-pipeline-ui (as described here)

---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: allow-namespace-2-kubeflow-edit
  ## this RoleBinding is in `namespace-1`, because it grants access to `namespace-1`
  namespace: kubeflow
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: kubeflow-edit
subjects:
  - kind: ServiceAccount
    name: default-editor
    ## the ServiceAccount lives in `namespace-2`
    namespace: kubeflow-user-example-com

However the error still persists. What am I missing?

Thanks

[ptitzler]

@thesuperzapper do you have any insights to share that might help in this case? I don’t think this is an Elyra specific configuration issue.