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From "Zhankun Tang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (YARN-6043) [HDL] Tensorflow on YARN
Date Fri, 30 Dec 2016 14:51:58 GMT

     [ https://issues.apache.org/jira/browse/YARN-6043?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Zhankun Tang updated YARN-6043:
-------------------------------
    Attachment:     (was: TensorFlow_on_YARN_proposal.pdf)

> [HDL] Tensorflow on YARN
> ------------------------
>
>                 Key: YARN-6043
>                 URL: https://issues.apache.org/jira/browse/YARN-6043
>             Project: Hadoop YARN
>          Issue Type: New Feature
>            Reporter: Kai Zheng
>
> As discussed in the umbrella HADOOP-13944, we'd like to work and support Deep Learning
on Hadoop. As a beginning, we implemented a prototype running Tensorflow on YARN. Preliminarily
the work provides a tool yarn-tf allowing users to submit and run a Tensorflow job (say mnist.py)
in a YARN cluster. It allocates and launches a Tensorflow cluster in YARN dynamically, executing
the job, and then destroys the cluster after the work is done. It doesn't require Python and
Tensorflow binary installations be done previously on YARN nodes (on client host, Python is
required if the job is written in Python). It doesn't go in the Docker approach. Given an
existing Hadoop cluster, it's pretty easy to run a Tensorflow job using the provided yarn-tf.jar
bundle (the TF core library and our JNI wrapper) and yarn-tf tool.
> In this jira we'll post our design documenting how we did it and the general approach.
The prototype work is under polishing and will be public here soon.
> Filing this as unassigned as it's a team work. Your thoughts and feedback are welcome.



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