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From "Wangda Tan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (YARN-7224) Support GPU isolation for docker container
Date Wed, 25 Oct 2017 01:46:00 GMT

     [ https://issues.apache.org/jira/browse/YARN-7224?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Wangda Tan updated YARN-7224:
    Attachment: YARN-7224.007.patch

Attached ver.7 patch, fixed warnings / javadocs, UT failure is not related.

> Support GPU isolation for docker container
> ------------------------------------------
>                 Key: YARN-7224
>                 URL: https://issues.apache.org/jira/browse/YARN-7224
>             Project: Hadoop YARN
>          Issue Type: Sub-task
>            Reporter: Wangda Tan
>            Assignee: Wangda Tan
>         Attachments: YARN-7224.001.patch, YARN-7224.002-wip.patch, YARN-7224.003.patch,
YARN-7224.004.patch, YARN-7224.005.patch, YARN-7224.006.patch, YARN-7224.007.patch
> This patch is to address issues when docker container is being used:
> 1. GPU driver and nvidia libraries: If GPU drivers and NV libraries are pre-packaged
inside docker image, it could conflict to driver and nvidia-libraries installed on Host OS.
An alternative solution is to detect Host OS's installed drivers and devices, mount it when
launch docker container. Please refer to \[1\] for more details. 
> 2. Image detection: 
> From \[2\], the challenge is: 
> bq. Mounting user-level driver libraries and device files clobbers the environment of
the container, it should be done only when the container is running a GPU application. The
challenge here is to determine if a given image will be using the GPU or not. We should also
prevent launching containers based on a Docker image that is incompatible with the host NVIDIA
driver version, you can find more details on this wiki page.
> 3. GPU isolation.
> *Proposed solution*:
> a. Use nvidia-docker-plugin \[3\] to address issue #1, this is the same solution used
by K8S \[4\]. issue #2 could be addressed in a separate JIRA.
> We won't ship nvidia-docker-plugin with out releases and we require cluster admin to
preinstall nvidia-docker-plugin to use GPU+docker support on YARN. "nvidia-docker" is a wrapper
of docker binary which can address #3 as well, however "nvidia-docker" doesn't provide same
semantics of docker, and it needs to setup additional environments such as PATH/LD_LIBRARY_PATH
to use it. To avoid introducing additional issues, we plan to use nvidia-docker-plugin + docker
binary approach.
> b. To address GPU driver and nvidia libraries, we uses nvidia-docker-plugin \[3\] to
create a volume which includes GPU-related libraries and mount it when docker container being
launched. Changes include: 
> - Instead of using {{volume-driver}}, this patch added {{docker volume create}} command
to c-e and NM Java side. The reason is {{volume-driver}} can only use single volume driver
for each launched docker container.
> - Updated {{c-e}} and Java side, if a mounted volume is a named volume in docker, skip
checking file existence. (Named-volume still need to be added to permitted list of container-executor.cfg).
> c. To address isolation issue:
> We found that, cgroup + docker doesn't work under newer docker version which uses {{runc}}
as default runtime. Setting {{--cgroup-parent}} to a cgroup which include any {{devices.deny}}
causes docker container cannot be launched.
> Instead this patch passes allowed GPU devices via {{--device}} to docker launch command.
> References:
> \[1\] https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-driver
> \[2\] https://github.com/NVIDIA/nvidia-docker/wiki/Image-inspection
> \[3\] https://github.com/NVIDIA/nvidia-docker/wiki/nvidia-docker-plugin
> \[4\] https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/

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