[jira] [Resolved] (IGNITE-8335) TensorFlow integration

["Yury Babak (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/IGNITE-8335?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yury Babak resolved IGNITE-8335.
--------------------------------
       Resolution: Done
    Fix Version/s: 2.7

done in other tasks

> TensorFlow integration
> ----------------------
>
>                 Key: IGNITE-8335
>                 URL: https://issues.apache.org/jira/browse/IGNITE-8335
>             Project: Ignite
>          Issue Type: New Feature
>          Components: ml
>            Reporter: Yury Babak
>            Assignee: Anton Dmitriev
>            Priority: Major
>             Fix For: 2.7
>
>
> The goal of TensorFlow on Apache Ignite is to allow users to train and inference neural network models on a data stored in Apache Ignite distributed database utilizing all TensorFlow functionality and Apache Ignite data collocation abilities.
> There are 8 questions we need to answer to build TensorFlow on Apache Ignite:
> * How to build and maintain TensorFlow cluster on top of Apache Ignite infrastructure utilizing Apache Ignite data collocation abilities?
> * How to pass data from Apache Ignite storage into TensorFlow?
> * How to organize load balancing and optimize Apache Ignite data distribution to improve training performance?
> * How to integrate TensorFlow checkpoints mechanism into Apache Ignite ecosystem?
> * How to recover after cluster node failures?
> * How to deploy TensorFlow on Apache Ignite in local/cluster/cloud environment?
> * How to serve model built in TensorFlow on Apache Ignite?
> * How to profile training using TensorBoard or similar tools?



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