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Date: Tue, 24 Oct 2017 19:24:00 +0000 (UTC)
From: "Wangda Tan (JIRA)" <jira@apache.org>
To: yarn-issues@hadoop.apache.org
Message-ID: <JIRA.13103436.1505862150000.49088.1508873040419@Atlassian.JIRA>
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Subject: [jira] [Updated] (YARN-7224) Support GPU isolation for docker
 container
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archived-at: Tue, 24 Oct 2017 19:24:06 -0000


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

Wangda Tan updated YARN-7224:
-----------------------------
    Attachment: YARN-7224.006.patch

Attached ver.6 patch to run Jenkins.

> 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
>
>
> 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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