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https://issues.apache.org/jira/browse/SPARK-22658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16271930#comment-16271930
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Sean Owen commented on SPARK-22658:
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I don't see a strong reason this needs to be part of Spark. It shifts
maintenance to the core project for not much of any gain. It also tends to
bless a single deep-learning-on-Spark project among several. I would say 'no'
to this, but instead focus on whatever changes in the core help support
libraries like this (like the image representation SPIP recently)
> SPIP: TeansorFlowOnSpark as a Scalable Deep Learning Lib of Apache Spark
> ------------------------------------------------------------------------
>
> Key: SPARK-22658
> URL: https://issues.apache.org/jira/browse/SPARK-22658
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Affects Versions: 2.2.0
> Reporter: Andy Feng
> Attachments: SPIP_ TensorFlowOnSpark.pdf
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> TensorFlowOnSpark (TFoS) was released at github for distributed TensorFlow
> training and inference on Apache Spark clusters. TFoS is designed to:
> * Easily migrate all existing TensorFlow programs with minimum code change;
> * Support all TensorFlow functionalities: synchronous/asynchronous training,
> model/data parallelism, inference and TensorBoard;
> * Easily integrate with your existing data processing pipelines (ex. Spark
> SQL) and machine learning algorithms (ex. MLlib);
> * Be easily deployed on cloud or on-premise: CPU & GPU, Ethernet and
> Infiniband.
> We propose to merge TFoS into Apache Spark as a scalable deep learning
> library to:
> * Make deep learning easy for Apache Spark community: Familiar pipeline API
> for training and inference; Enable TensorFlow training/inference on existing
> Spark clusters.
> * Further simplify data scientist experience: Ensure compatibility b/w Apache
> Spark and TFoS; Reduce steps for installation.
> * Help Apache Spark evolutions on deep learning: Establish a design pattern
> for additional frameworks (ex. Caffe, CNTK); Structured streaming for DL
> training/inference.
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