Code and jobs to cleanup the the new auto-deployed blueprints.

[jlewi]

• We want to auto-deploy the GCP blueprint (Setup autodeploy for GCP blueprints GoogleCloudPlatform/kubeflow-distribution#5)

• We need to add logic and K8s resources to cleanup the blueprints so
  we don't run out of GCP quota.

• Create cleanup_blueprints.py to cleanup auto_deployed blueprints.

  • Don't put this code in cleanup_ci.py because we want to be able to
    use fire and possibly python3 (not sure code in cleanup_ci is python3
    compatible)

• Create a CLI create_context.py to create K8s config contexts. This will
  be used to get credentials to talk to the cleanup cluster when running
  on K8s.

• Create a Tekton task to run the cleanup script. This is intended
  as a replacement for our existing K8s job (Use Tekton (instead of K8s jobs) to cleanup the Kubeflow CI project #654). There's a couple reasons
  to start using Tekton

  i) We are already using Tekton as part of AutoDeploy infrastructure.
  ii) We can leverage Tekton to handle git checkouts.
  iii) Tekton makes it easy to additional steps to do things like
  create the context.

  • This is a partial solution. This PR contains a Tekton pipeline
    that is only running cleanup for the blueprints.

    • To do all cleanup using Tekton we just need to a step or Task to
      run the existing cleanup-ci script. The only issue I forsee
      is that the Tekton pipeline runs in the kf-ci-v1 cluster and
      will need to be granted access to the kubeflow-testing cluster
      so we can cleanup Argo workflows in that cluster.

• To run the Tekton pipeline regulary we create a cronjob that runs kubectl
  apply.

• cnrm_clients.py is a quick hack to create a wrapper to make it
  easier to work with CNRM custom resources.

[kubeflow-bot]

This change is 

[jlewi]

/assign @Bobgy

[Bobgy]

/lgtm

[jlewi]

/approve

[k8s-ci-robot]

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