[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: 3.0.0)
3.1.0
> 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: 3.1.0
>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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[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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[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
]
Jason Wang updated SPARK-27892:
---
Description:
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}
was: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.
> 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: 2.4.3
>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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