[
https://issues.apache.org/jira/browse/YARN-9294?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Keqiu Hu updated YARN-9294:
---------------------------
Description:
Environment is latest branch-2 head
OS: RHEL 7.4
*Observation*
Out of ~10 container allocations with GPU requirement, at least 1 of the
allocated containers would lose GPU isolation. Even if I asked for 1 GPU, I
could still have visibility to all GPUs on the same machine when running
nvidia-smi.
The funny thing is even though I have visibility to all GPUs at the moment of
executing container-executor (say ordinal 0,1,2,3), but cgroups jailed the
process's access to only that single GPU after sometime.
The underlying process trying to access GPU would take the initial information
as source of truth and try to access physical 0 GPU which is not really
available to the process. This results in a [CUDA_ERROR_INVALID_DEVICE: invalid
device ordinal] error.
Validated the container-executor commands are correct:
{code:java}
PrivilegedOperationExecutor command:
[/export/apps/hadoop/nodemanager/latest/bin/container-executor, --module-gpu,
--container_id, container_e22_1549663278916_0249_01_000001, --excluded_gpus,
0,1,2,3]
PrivilegedOperationExecutor command:
[/export/apps/hadoop/nodemanager/latest/bin/container-executor, khu, khu, 0,
application_1549663278916_0249,
/grid/a/tmp/yarn/nmPrivate/container_e22_1549663278916_0249_01_000001.tokens,
/grid/a/tmp/yarn, /grid/a/tmp/userlogs,
/export/apps/jdk/JDK-1_8_0_172/jre/bin/java, -classpath, ..., -Xmx256m,
org.apache.hadoop.yarn.server.nodemanager.containermanager.localizer.ContainerLocalizer,
khu, application_1549663278916_0249,
container_e22_1549663278916_0249_01_000001, ltx1-hcl7552.grid.linkedin.com,
8040, /grid/a/tmp/yarn]
{code}
So most likely a race condition between these two operations?
cc [~jhung]
Another potential theory is the cgroups creation for the container actually
failed but the error was swallowed silently.
was:
Environment is latest branch-2 head
OS: RHEL 7.4
*Observation*
Out of ~10 container allocations with GPU requirement, at least 1 of the
allocated containers would lose GPU isolation. Even if I asked for 1 GPU, I
could still have visibility to all GPUs on the same machine when running
nvidia-smi.
The funny thing is even though I have visibility to all GPUs at the moment of
executing container-executor (say ordinal 0,1,2,3), but cgroups jailed the
process's access to only that single GPU after sometime.
The underlying process trying to access GPU would take the initial information
as source of truth and try to access physical 0 GPU which is not really
available to the process. This results in a [CUDA_ERROR_INVALID_DEVICE: invalid
device ordinal] error.
Validated the container-executor commands are correct:
{code:java}
PrivilegedOperationExecutor command:
[/export/apps/hadoop/nodemanager/latest/bin/container-executor, --module-gpu,
--container_id, container_e22_1549663278916_0249_01_000001, --excluded_gpus,
0,1,2,3]
PrivilegedOperationExecutor command:
[/export/apps/hadoop/nodemanager/latest/bin/container-executor, khu, khu, 0,
application_1549663278916_0249,
/grid/a/tmp/yarn/nmPrivate/container_e22_1549663278916_0249_01_000001.tokens,
/grid/a/tmp/yarn, /grid/a/tmp/userlogs,
/export/apps/jdk/JDK-1_8_0_172/jre/bin/java, -classpath, ..., -Xmx256m,
org.apache.hadoop.yarn.server.nodemanager.containermanager.localizer.ContainerLocalizer,
khu, application_1549663278916_0249,
container_e22_1549663278916_0249_01_000001, ltx1-hcl7552.grid.linkedin.com,
8040, /grid/a/tmp/yarn]
{code}
So most likely a race condition between these two operations?
cc [~jhung]
> Potential race condition in setting GPU cgroups & execute command in the
> selected cgroup
> ----------------------------------------------------------------------------------------
>
> Key: YARN-9294
> URL: https://issues.apache.org/jira/browse/YARN-9294
> Project: Hadoop YARN
> Issue Type: Bug
> Components: yarn
> Affects Versions: 2.10.0
> Reporter: Keqiu Hu
> Assignee: Keqiu Hu
> Priority: Critical
>
> Environment is latest branch-2 head
> OS: RHEL 7.4
> *Observation*
> Out of ~10 container allocations with GPU requirement, at least 1 of the
> allocated containers would lose GPU isolation. Even if I asked for 1 GPU, I
> could still have visibility to all GPUs on the same machine when running
> nvidia-smi.
> The funny thing is even though I have visibility to all GPUs at the moment of
> executing container-executor (say ordinal 0,1,2,3), but cgroups jailed the
> process's access to only that single GPU after sometime.
> The underlying process trying to access GPU would take the initial
> information as source of truth and try to access physical 0 GPU which is not
> really available to the process. This results in a
> [CUDA_ERROR_INVALID_DEVICE: invalid device ordinal] error.
> Validated the container-executor commands are correct:
> {code:java}
> PrivilegedOperationExecutor command:
> [/export/apps/hadoop/nodemanager/latest/bin/container-executor, --module-gpu,
> --container_id, container_e22_1549663278916_0249_01_000001, --excluded_gpus,
> 0,1,2,3]
> PrivilegedOperationExecutor command:
> [/export/apps/hadoop/nodemanager/latest/bin/container-executor, khu, khu, 0,
> application_1549663278916_0249,
> /grid/a/tmp/yarn/nmPrivate/container_e22_1549663278916_0249_01_000001.tokens,
> /grid/a/tmp/yarn, /grid/a/tmp/userlogs,
> /export/apps/jdk/JDK-1_8_0_172/jre/bin/java, -classpath, ..., -Xmx256m,
> org.apache.hadoop.yarn.server.nodemanager.containermanager.localizer.ContainerLocalizer,
> khu, application_1549663278916_0249,
> container_e22_1549663278916_0249_01_000001, ltx1-hcl7552.grid.linkedin.com,
> 8040, /grid/a/tmp/yarn]
> {code}
> So most likely a race condition between these two operations?
> cc [~jhung]
> Another potential theory is the cgroups creation for the container actually
> failed but the error was swallowed silently.
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