[Snyk] Security upgrade python from 3.7-slim to 3.13-slim

[dsp-developers]

Snyk has created this PR to fix 3 vulnerabilities in the dockerfile dependencies of this project.

Keeping your Docker base image up-to-date means you’ll benefit from security fixes in the latest version of your chosen image.

Snyk changed the following file(s):

• samples/contrib/versioned-pipeline-ci-samples/mnist-ci-sample/tensorboard/Dockerfile

We recommend upgrading to python:3.13-slim, as this image has only 37 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

Vulnerabilities that will be fixed with an upgrade:

	Issue	Score
	Out-of-bounds Write SNYK-DEBIAN12-GLIBC-5927132	829
	Out-of-bounds Write SNYK-DEBIAN12-GLIBC-5927132	829
	Out-of-bounds Write SNYK-DEBIAN12-GLIBC-6210098	721
	Out-of-bounds Write SNYK-DEBIAN12-GLIBC-6210098	721
	CVE-2024-37371 SNYK-DEBIAN12-KRB5-7411315	714

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Important

• Check the changes in this PR to ensure they won't cause issues with your project.
• Max score is 1000. Note that the real score may have changed since the PR was raised.
• This PR was automatically created by Snyk using the credentials of a real user.

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[openshift-ci]

[APPROVALNOTIFIER] This PR is NOT APPROVED

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[dsp-developers]

Commit Checker results:

**NOTE**: These are the results of the commit checker scans. 
If these are not commits from upstream kfp, then please ensure
you adhere to the commit checker formatting

commitchecker verson unknown
Validating 1 commits between 57a28230fb3390fda3195efb99228bfc8d99c7f8...9c51d78d529c443d500f6947277ec6d704939cc7

UPSTREAM commit 9c51d78 has invalid summary fix: samples/contrib/versioned-pipeline-ci-samples/mnist-ci-sample/tensorboard/Dockerfile to reduce vulnerabilities.

UPSTREAM commits are validated against the following regular expression:
  ^UPSTREAM: (revert: )?(([\w.-]+/[\w-.-]+)?: )?(\d+:|<carry>:|<drop>:)

UPSTREAM commit summaries should look like:

  UPSTREAM: <PR number|carry|drop>: description

UPSTREAM commits which revert previous UPSTREAM commits should look like:

  UPSTREAM: revert: <normal upstream format>

Examples of valid summaries:

  UPSTREAM: 12345: A kube fix
  UPSTREAM: <carry>: A carried kube change
  UPSTREAM: <drop>: A dropped kube change
  UPSTREAM: revert: 12345: A kube revert

[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-93
DSP DRIVER: quay.io/opendatahub/ds-pipelines-driver:pr-93
DSP LAUNCHER: quay.io/opendatahub/ds-pipelines-launcher:pr-93
Persistence Agent: quay.io/opendatahub/ds-pipelines-persistenceagent:pr-93
Scheduled Workflow Manager: quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-93
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-93

[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-93.yaml:

apiVersion: datasciencepipelinesapplications.opendatahub.io/v1alpha1
kind: DataSciencePipelinesApplication
metadata:
  name: pr-93
spec:
  dspVersion: v2
  apiServer:
    image: "quay.io/opendatahub/ds-pipelines-api-server:pr-93"
    argoDriverImage: "quay.io/opendatahub/ds-pipelines-driver:pr-93"
    argoLauncherImage: "quay.io/opendatahub/ds-pipelines-launcher:pr-93"
  persistenceAgent:
    image: "quay.io/opendatahub/ds-pipelines-persistenceagent:pr-93"
  scheduledWorkflow:
    image: "quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-93"
  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-93"
  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/93/head
git checkout -b pullrequest 9c51d78d529c443d500f6947277ec6d704939cc7
oc apply -f dspa.pr-93.yaml

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