Repository: spark Updated Branches: refs/heads/branch-1.3 4ff577160 -> bc92a2e40
[SPARK-5955][MLLIB] add checkpointInterval to ALS Add checkpiontInterval to ALS to prevent: 1. StackOverflow exceptions caused by long lineage, 2. large shuffle files generated during iterations, 3. slow recovery when some node fail. srowen coderxiang Author: Xiangrui Meng <[email protected]> Closes #5076 from mengxr/SPARK-5955 and squashes the following commits: df56791 [Xiangrui Meng] update impl to reuse code 29affcb [Xiangrui Meng] do not materialize factors in implicit 20d3f7f [Xiangrui Meng] add checkpointInterval to ALS (cherry picked from commit 6b36470c66bd6140c45e45d3f1d51b0082c3fd97) Signed-off-by: Xiangrui Meng <[email protected]> Conflicts: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/bc92a2e4 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/bc92a2e4 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/bc92a2e4 Branch: refs/heads/branch-1.3 Commit: bc92a2e405241542770a64adfef39dcb02e96461 Parents: 4ff5771 Author: Xiangrui Meng <[email protected]> Authored: Fri Mar 20 15:02:57 2015 -0400 Committer: Xiangrui Meng <[email protected]> Committed: Tue Mar 24 11:32:18 2015 -0700 ---------------------------------------------------------------------- .../apache/spark/ml/param/sharedParams.scala | 11 +++++ .../apache/spark/ml/recommendation/ALS.scala | 42 +++++++++++++++++--- .../apache/spark/mllib/recommendation/ALS.scala | 17 ++++++++ .../spark/ml/recommendation/ALSSuite.scala | 17 ++++++++ 4 files changed, 82 insertions(+), 5 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/bc92a2e4/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala index 1a70322..5d660d1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala @@ -138,3 +138,14 @@ private[ml] trait HasOutputCol extends Params { /** @group getParam */ def getOutputCol: String = get(outputCol) } + +private[ml] trait HasCheckpointInterval extends Params { + /** + * param for checkpoint interval + * @group param + */ + val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval") + + /** @group getParam */ + def getCheckpointInterval: Int = get(checkpointInterval) +} http://git-wip-us.apache.org/repos/asf/spark/blob/bc92a2e4/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index 7bb69df..058076d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -18,6 +18,7 @@ package org.apache.spark.ml.recommendation import java.{util => ju} +import java.io.IOException import scala.collection.mutable import scala.reflect.ClassTag @@ -26,6 +27,7 @@ import scala.util.hashing.byteswap64 import com.github.fommil.netlib.BLAS.{getInstance => blas} import com.github.fommil.netlib.LAPACK.{getInstance => lapack} +import org.apache.hadoop.fs.{FileSystem, Path} import org.jblas.DoubleMatrix import org.netlib.util.intW @@ -47,7 +49,7 @@ import org.apache.spark.util.random.XORShiftRandom * Common params for ALS. */ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasRegParam - with HasPredictionCol { + with HasPredictionCol with HasCheckpointInterval { /** * Param for rank of the matrix factorization. @@ -165,6 +167,7 @@ class ALSModel private[ml] ( itemFactors: RDD[(Int, Array[Float])]) extends Model[ALSModel] with ALSParams { + /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { @@ -263,6 +266,9 @@ class ALS extends Estimator[ALSModel] with ALSParams { /** @group setParam */ def setNonnegative(value: Boolean): this.type = set(nonnegative, value) + /** @group setParam */ + def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) + /** * Sets both numUserBlocks and numItemBlocks to the specific value. * @group setParam @@ -275,6 +281,7 @@ class ALS extends Estimator[ALSModel] with ALSParams { setMaxIter(20) setRegParam(1.0) + setCheckpointInterval(10) override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { val map = this.paramMap ++ paramMap @@ -286,7 +293,8 @@ class ALS extends Estimator[ALSModel] with ALSParams { val (userFactors, itemFactors) = ALS.train(ratings, rank = map(rank), numUserBlocks = map(numUserBlocks), numItemBlocks = map(numItemBlocks), maxIter = map(maxIter), regParam = map(regParam), implicitPrefs = map(implicitPrefs), - alpha = map(alpha), nonnegative = map(nonnegative)) + alpha = map(alpha), nonnegative = map(nonnegative), + checkpointInterval = map(checkpointInterval)) val model = new ALSModel(this, map, map(rank), userFactors, itemFactors) Params.inheritValues(map, this, model) model @@ -496,6 +504,7 @@ object ALS extends Logging { nonnegative: Boolean = false, intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, + checkpointInterval: Int = 10, seed: Long = 0L)( implicit ord: Ordering[ID]): (RDD[(ID, Array[Float])], RDD[(ID, Array[Float])]) = { require(intermediateRDDStorageLevel != StorageLevel.NONE, @@ -523,6 +532,18 @@ object ALS extends Logging { val seedGen = new XORShiftRandom(seed) var userFactors = initialize(userInBlocks, rank, seedGen.nextLong()) var itemFactors = initialize(itemInBlocks, rank, seedGen.nextLong()) + var previousCheckpointFile: Option[String] = None + val shouldCheckpoint: Int => Boolean = (iter) => + sc.checkpointDir.isDefined && (iter % checkpointInterval == 0) + val deletePreviousCheckpointFile: () => Unit = () => + previousCheckpointFile.foreach { file => + try { + FileSystem.get(sc.hadoopConfiguration).delete(new Path(file), true) + } catch { + case e: IOException => + logWarning(s"Cannot delete checkpoint file $file:", e) + } + } if (implicitPrefs) { for (iter <- 1 to maxIter) { userFactors.setName(s"userFactors-$iter").persist(intermediateRDDStorageLevel) @@ -530,19 +551,30 @@ object ALS extends Logging { itemFactors = computeFactors(userFactors, userOutBlocks, itemInBlocks, rank, regParam, userLocalIndexEncoder, implicitPrefs, alpha, solver) previousItemFactors.unpersist() - if (sc.checkpointDir.isDefined && (iter % 3 == 0)) { - itemFactors.checkpoint() - } itemFactors.setName(s"itemFactors-$iter").persist(intermediateRDDStorageLevel) + // TODO: Generalize PeriodicGraphCheckpointer and use it here. + if (shouldCheckpoint(iter)) { + itemFactors.checkpoint() // itemFactors gets materialized in computeFactors. + } val previousUserFactors = userFactors userFactors = computeFactors(itemFactors, itemOutBlocks, userInBlocks, rank, regParam, itemLocalIndexEncoder, implicitPrefs, alpha, solver) + if (shouldCheckpoint(iter)) { + deletePreviousCheckpointFile() + previousCheckpointFile = itemFactors.getCheckpointFile + } previousUserFactors.unpersist() } } else { for (iter <- 0 until maxIter) { itemFactors = computeFactors(userFactors, userOutBlocks, itemInBlocks, rank, regParam, userLocalIndexEncoder, solver = solver) + if (shouldCheckpoint(iter)) { + itemFactors.checkpoint() + itemFactors.count() // checkpoint item factors and cut lineage + deletePreviousCheckpointFile() + previousCheckpointFile = itemFactors.getCheckpointFile + } userFactors = computeFactors(itemFactors, itemOutBlocks, userInBlocks, rank, regParam, itemLocalIndexEncoder, solver = solver) } http://git-wip-us.apache.org/repos/asf/spark/blob/bc92a2e4/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index caacab9..dddefe1 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -82,6 +82,9 @@ class ALS private ( private var intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK private var finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK + /** checkpoint interval */ + private var checkpointInterval: Int = 10 + /** * Set the number of blocks for both user blocks and product blocks to parallelize the computation * into; pass -1 for an auto-configured number of blocks. Default: -1. @@ -183,6 +186,19 @@ class ALS private ( } /** + * Set period (in iterations) between checkpoints (default = 10). Checkpointing helps with + * recovery (when nodes fail) and StackOverflow exceptions caused by long lineage. It also helps + * with eliminating temporary shuffle files on disk, which can be important when there are many + * ALS iterations. If the checkpoint directory is not set in [[org.apache.spark.SparkContext]], + * this setting is ignored. + */ + @DeveloperApi + def setCheckpointInterval(checkpointInterval: Int): this.type = { + this.checkpointInterval = checkpointInterval + this + } + + /** * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples. * Returns a MatrixFactorizationModel with feature vectors for each user and product. */ @@ -212,6 +228,7 @@ class ALS private ( nonnegative = nonnegative, intermediateRDDStorageLevel = intermediateRDDStorageLevel, finalRDDStorageLevel = StorageLevel.NONE, + checkpointInterval = checkpointInterval, seed = seed) val userFactors = floatUserFactors http://git-wip-us.apache.org/repos/asf/spark/blob/bc92a2e4/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala index bb86baf..0bb06e9 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.ml.recommendation +import java.io.File import java.util.Random import scala.collection.mutable @@ -32,16 +33,25 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.rdd.RDD import org.apache.spark.sql.{Row, SQLContext} +import org.apache.spark.util.Utils class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { private var sqlContext: SQLContext = _ + private var tempDir: File = _ override def beforeAll(): Unit = { super.beforeAll() + tempDir = Utils.createTempDir() + sc.setCheckpointDir(tempDir.getAbsolutePath) sqlContext = new SQLContext(sc) } + override def afterAll(): Unit = { + Utils.deleteRecursively(tempDir) + super.afterAll() + } + test("LocalIndexEncoder") { val random = new Random for (numBlocks <- Seq(1, 2, 5, 10, 20, 50, 100)) { @@ -485,4 +495,11 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { }.count() } } + + test("als with large number of iterations") { + val (ratings, _) = genExplicitTestData(numUsers = 4, numItems = 4, rank = 1) + ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2) + ALS.train( + ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, implicitPrefs = true) + } } --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
