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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2017/09/20 06:07:00 UTC
[jira] [Created] (SPARK-22075) GBTs forgot to unpersist datasets
cached by PeriodicRDDCheckpointer
zhengruifeng created SPARK-22075:
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Summary: 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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