[ https://issues.apache.org/jira/browse/SPARK-5807?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Peter Rudenko updated SPARK-5807: --------------------------------- Description: Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers & memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} val metrics = val metrics = new ArrayBuffer[Double](numModels) with mutable.SynchronizedBuffer[Double] val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) => { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(s"Train split $splitIndex with multiple sets of parameters.") val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i < numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(s"Got metric $metric for model trained with ${epm(i)}.") metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations (pretty much memory), so maybe it's possible to cache each fold separately. was: Right now in CrossValidator for each fold combination and ParamGrid hyperparameter pair it searches the best parameter sequentially. Assuming there's enough workers & memory on a cluster to cache all training/validation folds it's possible to parallelize execution. Here's a draft i came with: {code} import scala.collection.immutable.{ Vector => ScalaVec } .... val metrics = ScalaVec.fill(numModels)(0.0) //Scala vector is thread safe val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { case ((training, validation), splitIndex) => { val trainingDataset = sqlCtx.applySchema(training, schema).cache() val validationDataset = sqlCtx.applySchema(validation, schema).cache() // multi-model training logDebug(s"Train split $splitIndex with multiple sets of parameters.") val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] var i = 0 trainingDataset.unpersist() while (i < numModels) { val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) logDebug(s"Got metric $metric for model trained with ${epm(i)}.") metrics(i) += metric i += 1 } validationDataset.unpersist() } } if (parallel) { splits.par.foreach(processFold) } else { splits.foreach(processFold) } {code} Assuming there's 3 folds it would redundantly cache all the combinations (pretty much memory), so maybe it's possible to cache each fold separately. > Parallel grid search > --------------------- > > Key: SPARK-5807 > URL: https://issues.apache.org/jira/browse/SPARK-5807 > Project: Spark > Issue Type: New Feature > Components: ML > Affects Versions: 1.3.0 > Reporter: Peter Rudenko > Priority: Minor > > Right now in CrossValidator for each fold combination and ParamGrid > hyperparameter pair it searches the best parameter sequentially. Assuming > there's enough workers & memory on a cluster to cache all training/validation > folds it's possible to parallelize execution. Here's a draft i came with: > {code} > val metrics = val metrics = new ArrayBuffer[Double](numModels) with > mutable.SynchronizedBuffer[Double] > val splits = MLUtils.kFold(dataset, map(numFolds), 0).zipWithIndex > def processFold(input: ((RDD[sql.Row], RDD[sql.Row]), Int)) = input match { > case ((training, validation), splitIndex) => { > val trainingDataset = sqlCtx.applySchema(training, schema).cache() > val validationDataset = sqlCtx.applySchema(validation, schema).cache() > // multi-model training > logDebug(s"Train split $splitIndex with multiple sets of parameters.") > val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]] > var i = 0 > trainingDataset.unpersist() > while (i < numModels) { > val metric = eval.evaluate(models(i).transform(validationDataset, > epm(i)), map) > logDebug(s"Got metric $metric for model trained with ${epm(i)}.") > metrics(i) += metric > i += 1 > } > validationDataset.unpersist() > } > } > if (parallel) { > splits.par.foreach(processFold) > } else { > splits.foreach(processFold) > } > {code} > Assuming there's 3 folds it would redundantly cache all the combinations > (pretty much memory), so maybe it's possible to cache each fold separately. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org