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https://issues.apache.org/jira/browse/SPARK-4587?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14306002#comment-14306002
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Sean Owen commented on SPARK-4587:
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You can get hundreds of megabytes of XML, yeah. I had thought to myself that, 
after compression, any representation of a huge forest is probably going to be 
the same order of magnitude.

There's definitely a need to define how to serialize the distributed data since 
it seems like must live outside any model file format (?) For example I was 
using a simple text-based format, which isn't exactly ideal compared to Parquet 
perhaps. I suppose what I was questioning was serializing the model itself with 
a new format. For example, a newly-invented clustering model format doesn't 
seem to add much.

> Model export/import
> -------------------
>
>                 Key: SPARK-4587
>                 URL: https://issues.apache.org/jira/browse/SPARK-4587
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, MLlib
>            Reporter: Xiangrui Meng
>            Assignee: Joseph K. Bradley
>            Priority: Critical
>
> This is an umbrella JIRA for one of the most requested features on the user 
> mailing list. Model export/import can be done via Java serialization. But it 
> doesn't work for models stored distributively, e.g., ALS and LDA. Ideally, we 
> should provide save/load methods to every model. PMML is an option but it has 
> its limitations. There are couple things we need to discuss: 1) data format, 
> 2) how to preserve partitioning, 3) data compatibility between versions and 
> language APIs, etc.
> UPDATE: [Design doc for model import/export | 
> https://docs.google.com/document/d/1kABFz1ssKJxLGMkboreSl3-I2CdLAOjNh5IQCrnDN3g/edit?usp=sharing]
> This document sketches machine learning model import/export plans, including 
> goals, an API, and development plans.
> The design doc proposes:
> * Support our own Spark-specific format.
> ** This is needed to (a) support distributed models and (b) get model 
> import/export support into Spark quickly (while avoiding new dependencies).
> * Also support PMML
> ** This is needed since it is the only thing approaching an industry standard.



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