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fix: tfjob with restartPolicy=ExitCode not work #1562
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cheimu:fix/tfjob-restartpolicy-exitcode-not-work
Jul 25, 2022
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Should we reset the value of existingRestartingCondition after this line?
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Hi @richardsliu, I don't know if we could reset it. I record the existingRestartingCondition outside the loop, which is the condition and result for current
reconcile()
. Inside the loop, when iterate over all pods, if there are multiple workers or masters .etc, are running, and only one worker failed, by the logic inside ofupdateJobConditions()
,restarting
+running
combination will makerunning
condition overwriterestarting
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@richardsliu is this the right way to deal with it? If one of the pods is restarting, do we mark the whole job as restarting ?
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If we do something along the lines of, would it achieve the same thing?
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Hi @pavanky, I would like to hear more advices: ) For now, I just follow the current convention. See https://github.com/kubeflow/training-operator/blob/master/pkg/controller.v1/tensorflow/tfjob_controller.go#L762 . Current strategy is that if one of the pods failed, then update the whole job status to failed : /
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Wouldn't that technically mean we are updating the job status to restart and incrementing the count two times for the same failure ?
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IMO, we could see it from 2 points of views.
running
, and one of roles got failed pods, then the whole job will be updated tofailed
, then in next reconcile, the pods will be deleted, so jobstatus should have arestarting
(don't know if I understand the code base correctly, we don't have specific restarting logic but use general reconcile logic)