UPSTREAM: <carry>: feat: mount EmptyDir volumes for launcher write locations pt 2

[gregsheremeta]

follows up on dfe6edc, which does an EmptyDir mount for /.local and /.cache. /.config is another common write location, so EmptyDir mount that too.

To test, verify that this pipeline now works:
https://github.com/kubeflow/pipelines/blob/master/samples/v2/producer_consumer_param.py

Checklist:

• The title for your pull request (PR) should follow our title convention. Learn more about the pull request title convention used in this repository.

[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-32
DSP DRIVER: quay.io/opendatahub/ds-pipelines-driver:pr-32
DSP LAUNCHER: quay.io/opendatahub/ds-pipelines-launcher:pr-32
Persistence Agent: quay.io/opendatahub/ds-pipelines-persistenceagent:pr-32
Scheduled Workflow Manager: quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-32
MLMD Server: quay.io/opendatahub/mlmd-grpc-server:latest
MLMD Envoy Proxy: registry.redhat.io/openshift-service-mesh/proxyv2-rhel8:2.3.9-2
UI: quay.io/opendatahub/ds-pipelines-frontend:pr-32

[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-32.yaml:

apiVersion: datasciencepipelinesapplications.opendatahub.io/v1alpha1
kind: DataSciencePipelinesApplication
metadata:
  name: pr-32
spec:
  dspVersion: v2
  apiServer:
    image: "quay.io/opendatahub/ds-pipelines-api-server:pr-32"
    argoDriverImage: "quay.io/opendatahub/ds-pipelines-driver:pr-32"
    argoLauncherImage: "quay.io/opendatahub/ds-pipelines-launcher:pr-32"
  persistenceAgent:
    image: "quay.io/opendatahub/ds-pipelines-persistenceagent:pr-32"
  scheduledWorkflow:
    image: "quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-32"
  mlmd:  
    deploy: true  # Optional component
    grpc:
      image: "quay.io/opendatahub/mlmd-grpc-server:latest"
    envoy:
      image: "registry.redhat.io/openshift-service-mesh/proxyv2-rhel8:2.3.9-2"
  mlpipelineUI:
    deploy: true  # Optional component 
    image: "quay.io/opendatahub/ds-pipelines-frontend:pr-32"
  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 git@github.com:opendatahub-io/data-science-pipelines.git
cd data-science-pipelines/
git fetch origin pull/32/head
git checkout -b pullrequest e428f162058b5bceab6689941d730ffb581f5ec8
oc apply -f dspa.pr-32.yaml

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

[HumairAK]

/lgtm
/approve

[openshift-ci]

[APPROVALNOTIFIER] This PR is APPROVED

This pull-request has been approved by: HumairAK

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