UPSTREAM: <carry>: remove direct k8s pkg import.

[HumairAK]

The k8s.io/kubernetes is not meant to be utilized as a library, we can just directly leverage conditions from the api library:

Use of the k8s.io/kubernetes module or k8s.io/kubernetes/... packages as libraries is not supported.

[HumairAK]

/hold

[openshift-ci]

[APPROVALNOTIFIER] This PR is APPROVED

This pull-request has been approved by: HumairAK

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[HumairAK]

to test this, please create a scheduled run

[dsp-developers]

A set of new images have been built to help with testing out this PR:
API Server: quay.io/opendatahub/ds-pipelines-api-server:pr-13
DSP DRIVER: quay.io/opendatahub/ds-pipelines-driver:pr-13
DSP LAUNCHER: quay.io/opendatahub/ds-pipelines-launcher:pr-13
Persistence Agent: quay.io/opendatahub/ds-pipelines-persistenceagent:pr-13
Scheduled Workflow Manager: quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-13
MLMD Server: quay.io/opendatahub/ds-pipelines-metadata-grpc:pr-13
MLMD Envoy Proxy: quay.io/opendatahub/ds-pipelines-metadata-envoy:pr-13
UI: quay.io/opendatahub/ds-pipelines-frontend:pr-13

[dsp-developers]

An OCP cluster where you are logged in as cluster admin is required.

The Data Science Pipelines team recommends testing this using the Data Science Pipelines Operator. Check here for more information on using the DSPO.

To use and deploy a DSP stack with these images (assuming the DSPO is deployed), first save the following YAML to a file named dspa.pr-13.yaml:

apiVersion: datasciencepipelinesapplications.opendatahub.io/v1alpha1
kind: DataSciencePipelinesApplication
metadata:
  name: pr-13
spec:
  dspVersion: v2
  apiServer:
    image: "quay.io/opendatahub/ds-pipelines-api-server:pr-13"
    argoDriverImage: "quay.io/opendatahub/ds-pipelines-driver:pr-13"
    argoLauncherImage: "quay.io/opendatahub/ds-pipelines-launcher:pr-13"
  persistenceAgent:
    image: "quay.io/opendatahub/ds-pipelines-persistenceagent:pr-13"
  scheduledWorkflow:
    image: "quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-13"
  mlmd:  
    deploy: true  # Optional component
    grpc:
      image: "quay.io/opendatahub/ds-pipelines-metadata-grpc:pr-13"
    envoy:
      image: "quay.io/opendatahub/ds-pipelines-metadata-envoy:pr-13"
  mlpipelineUI:
    deploy: true  # Optional component 
    image: "quay.io/opendatahub/ds-pipelines-frontend:pr-13"
  objectStorage:
    minio:
      deploy: true
      image: 'quay.io/opendatahub/minio:RELEASE.2019-08-14T20-37-41Z-license-compliance'

Then run the following:

cd $(mktemp -d)
git clone [email protected]:opendatahub-io/data-science-pipelines.git
cd data-science-pipelines/
git fetch origin pull/13/head
git checkout -b pullrequest bdf37e37d0823ad6ed175db9972cd1879ac8cda0
oc apply -f dspa.pr-13.yaml

More instructions here on how to deploy and test a Data Science Pipelines Application.

[gregsheremeta]

/lgtm

I verified that regular and scheduled runs are both working.

to test this, please create a scheduled run

how does testing scheduled runs test this patch? Is there something specific about scheduled run wrt k8s.io/kubernetes?

[HumairAK]

@gregsheremeta yes in this case it is being utilized in the scheduledworkflow crd status condition: https://github.com/kubeflow/pipelines/blob/bdf37e37d0823ad6ed175db9972cd1879ac8cda0/backend/src/crd/pkg/apis/scheduledworkflow/v1beta1/types.go#L184

when you create a scheduled run, this crd is created, if the status creation failed you'd probably would have encountered a failure to create this scheduled CR