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From "zhengruifeng (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-22075) GBTs forgot to unpersist datasets cached by Checkpointer
Date Wed, 20 Sep 2017 09:48:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-22075?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

zhengruifeng updated SPARK-22075:
---------------------------------
    Summary: GBTs forgot to unpersist datasets cached by Checkpointer  (was: GBTs/Pregel forgot
to unpersist datasets cached by Checkpointer)

> GBTs forgot to unpersist datasets cached by Checkpointer
> --------------------------------------------------------
>
>                 Key: SPARK-22075
>                 URL: https://issues.apache.org/jira/browse/SPARK-22075
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.3.0
>            Reporter: zhengruifeng
>
> {{PeriodicRDDCheckpointer}} will automatically persist the last 3 datasets called by
{{PeriodicRDDCheckpointer.update}}.
> In GBTs, the last 3 intermediate rdds are still cached after {{fit()}}
> {code}
> scala> val dataset = spark.read.format("libsvm").load("./data/mllib/sample_kmeans_data.txt")
> dataset: org.apache.spark.sql.DataFrame = [label: double, features: vector]     
> scala> dataset.persist()
> res0: dataset.type = [label: double, features: vector]
> scala> dataset.count
> res1: Long = 6
> scala> sc.getPersistentRDDs
> res2: scala.collection.Map[Int,org.apache.spark.rdd.RDD[_]] =
> Map(8 -> *FileScan libsvm [label#0,features#1] Batched: false, Format: LibSVM, Location:
InMemoryFileIndex[file:/Users/zrf/.dev/spark-2.2.0-bin-hadoop2.7/data/mllib/sample_kmeans_data.txt],
PartitionFilters: [], PushedFilters: [], ReadSchema: struct<label:double,features:struct<type:tinyint,size:int,indices:array<int>,values:array<double>>>
>  MapPartitionsRDD[8] at persist at <console>:26)
> scala> import org.apache.spark.ml.regression._
> import org.apache.spark.ml.regression._
> scala> val model = gbt.fit(dataset)
> <console>:28: error: not found: value gbt
>        val model = gbt.fit(dataset)
>                    ^
> scala> val gbt = new GBTRegressor()
> gbt: org.apache.spark.ml.regression.GBTRegressor = gbtr_da1fe371a25e
> scala> val model = gbt.fit(dataset)
> 17/09/20 14:05:33 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:35 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:35 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:35 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:35 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:35 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:36 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:36 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:36 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:36 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:36 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:36 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:37 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:37 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:37 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:37 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:37 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:37 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:38 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> 17/09/20 14:05:38 WARN DecisionTreeMetadata: DecisionTree reducing maxBins from 32 to
6 (= number of training instances)
> model: org.apache.spark.ml.regression.GBTRegressionModel = GBTRegressionModel (uid=gbtr_da1fe371a25e)
with 20 trees
> scala> sc.getPersistentRDDs
> res3: scala.collection.Map[Int,org.apache.spark.rdd.RDD[_]] =
> Map(322 -> MapPartitionsRDD[322] at mapPartitions at GradientBoostedTrees.scala:134,
307 -> MapPartitionsRDD[307] at mapPartitions at GradientBoostedTrees.scala:134, 8 ->
*FileScan libsvm [label#0,features#1] Batched: false, Format: LibSVM, Location: InMemoryFileIndex[file:/Users/zrf/.dev/spark-2.2.0-bin-hadoop2.7/data/mllib/sample_kmeans_data.txt],
PartitionFilters: [], PushedFilters: [], ReadSchema: struct<label:double,features:struct<type:tinyint,size:int,indices:array<int>,values:array<double>>>
>  MapPartitionsRDD[8] at persist at <console>:26, 292 -> MapPartitionsRDD[292]
at mapPartitions at GradientBoostedTrees.scala:134)
> scala> sc.getPersistentRDDs.size
> res4: Int = 4
> {code}



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