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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/09/20 06:10:00 UTC
[jira] [Assigned] (SPARK-22075) GBTs forgot to unpersist datasets
cached by PeriodicRDDCheckpointer
[ https://issues.apache.org/jira/browse/SPARK-22075?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-22075:
------------------------------------
Assignee: (was: Apache Spark)
> GBTs forgot to unpersist datasets cached by PeriodicRDDCheckpointer
> -------------------------------------------------------------------
>
> 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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