Skip flaky e2e MultiKueue test for XGBoost #2861
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Skipping CI for Draft Pull Request. |
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| @@ -633,6 +633,7 @@ var _ = ginkgo.Describe("MultiKueue", func() { | |||
| }) | |||
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| ginkgo.It("Should run a kubeflow XGBoostJob on worker if admitted", func() { | |||
| ginkgo.Skip("Skipped due to state transitioning bug in training-operator") | |||
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Put a comment with a link to the open issue in the training-operator repo
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I agree and I would have done that, but they don't have actual issue open, just the reference - kubeflow/training-operator#1711
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Let's use that issue
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I believe that only PyTorchJob e2e would be enough since we already commonized the KFJobs MK adapters into the kubeflowjob.MKAdapter in #2795.
So, after we merge this PR, can you refactor the e2e and integration tests for the KFJob MK adapter?
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Ok, then I will use this.
@tenzen-y Yes I will publish the test refactor - I pretty much have it.
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[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: alculquicondor, mszadkow The full list of commands accepted by this bot can be found here. The pull request process is described here
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* Skip flaky e2e multikueue test for XGBoost * add comment with the link issue
What type of PR is this?
/kind bug
What this PR does / why we need it:
Skips flaky test for e2e MultiKueue XGBoost, due to transitioning bug in training-operator.
Found here: kubeflow/training-operator#1711
Which issue(s) this PR fixes:
Part of #2838
Special notes for your reviewer:
Does this PR introduce a user-facing change?