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https://issues.apache.org/jira/browse/SPARK-4587?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-4587:
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Description:
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.
was: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.
> 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
> 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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