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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2019/06/09 20:13:00 UTC
[jira] [Updated] (SPARK-27892) Saving/loading stages in
PipelineModel should be parallel
[ https://issues.apache.org/jira/browse/SPARK-27892?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun updated SPARK-27892:
----------------------------------
Affects Version/s: (was: 2.4.3)
3.0.0
> Saving/loading stages in PipelineModel should be parallel
> ---------------------------------------------------------
>
> Key: SPARK-27892
> URL: https://issues.apache.org/jira/browse/SPARK-27892
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 3.0.0
> Reporter: Jason Wang
> Priority: Minor
> Labels: easyfix, performance
>
> When a PipelineModel is saved/loaded, all the stages are saved/loaded sequentially. When dealing with a PipelineModel with many stages, although each stage's save/load takes sub-second, the total time taken for the PipelineModel could be several minutes. It should be trivial to parallelize the save/load of stages in the SharedReadWrite object.
>
> To reproduce:
> {code:java}
> import org.apache.spark.ml._
> import org.apache.spark.ml.feature.VectorAssembler
> val outputPath = "..."
> val stages = (1 to 100) map { i => new VectorAssembler().setInputCols(Array("input")).setOutputCol("o" + i)}
> val p = new Pipeline().setStages(stages.toArray)
> val data = Seq(1, 1, 1) toDF "input"
> val pm = p.fit(data)
> pm.save(outputPath){code}
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