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From "Wangda Tan (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (YARN-8220) Running Tensorflow on YARN with GPU and Docker - Examples
Date Fri, 01 Jun 2018 17:17:00 GMT

    [ https://issues.apache.org/jira/browse/YARN-8220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16498270#comment-16498270
] 

Wangda Tan commented on YARN-8220:
----------------------------------

Thanks [~eyang] for your comments,

For your comments:
bq. 1. Avoid using bash style launch command 
Entry point is a nice feature for static command. (For example default TF docker image which
start notebook by default: https://github.com/tensorflow/tensorflow/tree/r1.8/tensorflow/tools/docker).

For training program, since user need to do a lot of hyper parameter tuning, user will update
such parameters to make it work. 

bq. 2. It might be good to show case some yarnfile features:
We intentionally want to avoid user specify this. It is a burden for user to specify such
mounting. In side submit_tf.py, we use the feature you mentioned. 

bq. 3. Downloading source code from individual github contributors might be risky and prone
to break
This is a good suggestion, will check if it is possible to commit example code to sub folder
of this example.

> Running Tensorflow on YARN with GPU and Docker - Examples
> ---------------------------------------------------------
>
>                 Key: YARN-8220
>                 URL: https://issues.apache.org/jira/browse/YARN-8220
>             Project: Hadoop YARN
>          Issue Type: Sub-task
>          Components: yarn-native-services
>            Reporter: Sunil Govindan
>            Assignee: Sunil Govindan
>            Priority: Critical
>         Attachments: YARN-8220.001.patch
>
>
> Tensorflow could be run on YARN and could leverage YARN's distributed features.
> This spec fill will help to run Tensorflow on yarn with GPU/docker



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