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https://issues.apache.org/jira/browse/YARN-8423?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16512022#comment-16512022
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Wangda Tan commented on YARN-8423:
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A possible simple fix to workaround the issue is to mark "releasing GPU" for
container in killing stage, and add a wait logic inside
{{GpuResourceAllocator#assignGpus}} before throw exception. But we may need a
comprehensive solution since we have more resources to add, and this is a
common severe issue for any resource need hard binding (like GPU / FPGA / CPU
hard-binding: YARN-8320, cc: [~cheersyang]).
Attached kill-container-nm.log which shows NM takes 2 mins to kill the (Docker)
container.
[~eyang], [~ebadger], [[email protected]], is it normal to wait minutes to
kill docker container? If yes, is there any way to speed it up? If no, what I
can provide to troubleshoot the issue? I saw this happen frequently in our
docker-in-docker setup.
> GPU does not get released even though the application gets killed.
> ------------------------------------------------------------------
>
> Key: YARN-8423
> URL: https://issues.apache.org/jira/browse/YARN-8423
> Project: Hadoop YARN
> Issue Type: Bug
> Components: yarn
> Reporter: Sumana Sathish
> Assignee: Wangda Tan
> Priority: Critical
> Attachments: kill-container-nm.log
>
>
> Run an Tensor flow app requesting one GPU.
> Kill the application once the GPU is allocated
> Query the nodemanger once the application is killed.We see that GPU is not
> being released.
> {code}
> curl -i <NM>/ws/v1/node/resources/yarn.io%2Fgpu
> {"gpuDeviceInformation":{"gpus":[{"productName":"<productName>","uuid":"GPU-<UID>","minorNumber":0,"gpuUtilizations":{"overallGpuUtilization":0.0},"gpuMemoryUsage":{"usedMemoryMiB":73,"availMemoryMiB":12125,"totalMemoryMiB":12198},"temperature":{"currentGpuTemp":28.0,"maxGpuTemp":85.0,"slowThresholdGpuTemp":82.0}},{"productName":"<productName>","uuid":"GPU-<UID>","minorNumber":1,"gpuUtilizations":{"overallGpuUtilization":0.0},"gpuMemoryUsage":{"usedMemoryMiB":73,"availMemoryMiB":12125,"totalMemoryMiB":12198},"temperature":{"currentGpuTemp":28.0,"maxGpuTemp":85.0,"slowThresholdGpuTemp":82.0}}],"driverVersion":"<version>"},"totalGpuDevices":[{"index":0,"minorNumber":0},{"index":1,"minorNumber":1}],"assignedGpuDevices":[{"index":0,"minorNumber":0,"containerId":"container_<containerID>"}]}
> {code}
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