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From "Yury Babak (JIRA)" <j...@apache.org>
Subject [jira] [Created] (IGNITE-8795) Add ability to start and maintain TensorFlow cluster on top of Apache Ignite
Date Thu, 14 Jun 2018 13:03:00 GMT
Yury Babak created IGNITE-8795:
----------------------------------

             Summary: Add ability to start and maintain TensorFlow cluster on top of Apache
Ignite
                 Key: IGNITE-8795
                 URL: https://issues.apache.org/jira/browse/IGNITE-8795
             Project: Ignite
          Issue Type: New Feature
          Components: ml
            Reporter: Yury Babak
            Assignee: Anton Dmitriev
             Fix For: 2.6


As described in the design document (https://docs.google.com/document/d/1jROIahK1rc7bSgOvhJhfpMqIGvht_IE8zn5NAt6x8ks/edit?usp=sharing)
Distributed TensorFlow is based on TensorFlow cluster concept. It's a set of TensorFlow processes
started among the cluster and available througth the gRPC interfaces. It's assumed that these
processes contain heavy operations that requires data to be stored locally on the nodes where
the processes running. Apache Ignite admits the data to be moved from one node to another
as result of node failure of rebalancing. As result the TensorFlow cluster should be changed
dynamically as well as TensorFlow Cache (follow-the-data strategy).



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