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[ML-116] use getOneCCLIPPort and fix lib loading #118

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Aug 12, 2021
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[ML-116] use getOneCCLIPPort and fix lib loading #118

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10 changes: 2 additions & 8 deletions mllib-dal/src/main/scala/org/apache/spark/ml/clustering/KMeansDALImpl.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ import com.intel.daal.services.DaalContext
import org.apache.spark.TaskContext
import org.apache.spark.internal.Logging
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.util.Utils.getOneCCLIPPort
import org.apache.spark.ml.util._
import org.apache.spark.mllib.clustering.{KMeansModel => MLlibKMeansModel}
import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
Expand All @@ -39,14 +40,7 @@ class KMeansDALImpl(var nClusters: Int,

val coalescedTables = OneDAL.rddVectorToMergedTables(data, executorNum)

val executorIPAddress = Utils.sparkFirstExecutorIP(coalescedTables.sparkContext)
val kvsIP = coalescedTables.sparkContext.conf.get("spark.oap.mllib.oneccl.kvs.ip",
executorIPAddress)
val kvsPortDetected = Utils.checkExecutorAvailPort(coalescedTables, kvsIP)
val kvsPort = coalescedTables.sparkContext.conf.getInt("spark.oap.mllib.oneccl.kvs.port",
kvsPortDetected)

val kvsIPPort = kvsIP + "_" + kvsPort
val kvsIPPort = getOneCCLIPPort(coalescedTables)

val sparkContext = data.sparkContext
val useGPU = sparkContext.conf.getBoolean("spark.oap.mllib.useGPU", false)
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11 changes: 2 additions & 9 deletions mllib-dal/src/main/scala/org/apache/spark/ml/feature/PCADALImpl.scala
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Expand Up @@ -21,6 +21,7 @@ import com.intel.daal.data_management.data.{HomogenNumericTable, NumericTable}
import org.apache.spark.TaskContext
import org.apache.spark.internal.Logging
import org.apache.spark.ml.linalg._
import org.apache.spark.ml.util.Utils.getOneCCLIPPort
import org.apache.spark.ml.util.{OneCCL, OneDAL, Utils}
import org.apache.spark.mllib.feature.{PCAModel => MLlibPCAModel, StandardScaler => MLlibStandardScaler}
import org.apache.spark.mllib.linalg.{DenseMatrix => OldDenseMatrix, Vectors => OldVectors}
Expand All @@ -37,15 +38,7 @@ class PCADALImpl(val k: Int,

val coalescedTables = OneDAL.rddVectorToMergedTables(normalizedData, executorNum)

val executorIPAddress = Utils.sparkFirstExecutorIP(coalescedTables.sparkContext)
val kvsIP = coalescedTables.sparkContext.conf.get("spark.oap.mllib.oneccl.kvs.ip",
executorIPAddress)

val kvsPortDetected = Utils.checkExecutorAvailPort(coalescedTables, kvsIP)
val kvsPort = coalescedTables.sparkContext.conf.getInt("spark.oap.mllib.oneccl.kvs.port",
kvsPortDetected)

val kvsIPPort = kvsIP + "_" + kvsPort
val kvsIPPort = getOneCCLIPPort(coalescedTables)

val sparkContext = data.sparkContext
val useGPU = sparkContext.conf.getBoolean("spark.oap.mllib.useGPU", false)
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18 changes: 5 additions & 13 deletions mllib-dal/src/main/scala/org/apache/spark/ml/recommendation/ALSDALImpl.scala
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Expand Up @@ -17,16 +17,15 @@
package org.apache.spark.ml.recommendation

import java.nio.{ByteBuffer, ByteOrder, FloatBuffer}

import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag

import com.intel.daal.data_management.data.CSRNumericTable
import com.intel.daal.services.DaalContext

import org.apache.spark.Partitioner
import org.apache.spark.internal.Logging
import org.apache.spark.ml.recommendation.ALS.Rating
import org.apache.spark.ml.util.LibLoader.loadLibraries
import org.apache.spark.ml.util.Utils.getOneCCLIPPort
import org.apache.spark.ml.util._
import org.apache.spark.rdd.RDD

Expand Down Expand Up @@ -77,23 +76,16 @@ class ALSDALImpl[@specialized(Int, Long) ID: ClassTag]( data: RDD[Rating[ID]],
val numericTables = data.repartition(executorNum)
.setName("Repartitioned for conversion").cache()

val executorIPAddress = Utils.sparkFirstExecutorIP(numericTables.sparkContext)
val kvsIP = numericTables.sparkContext.conf.get(
"spark.oap.mllib.oneccl.kvs.ip", executorIPAddress)

val kvsPortDetected = Utils.checkExecutorAvailPort(numericTables, kvsIP)
val kvsPort = numericTables.sparkContext.conf.getInt(
"spark.oap.mllib.oneccl.kvs.port", kvsPortDetected)

val kvsIPPort = kvsIP + "_" + kvsPort
val kvsIPPort = getOneCCLIPPort(numericTables)

val results = numericTables
// Transpose the dataset
.map { p =>
Rating(p.item, p.user, p.rating)
}
.mapPartitionsWithIndex { (rank, iter) =>
val context = new DaalContext()
// TODO: Use one-time init to load libraries
loadLibraries()

OneCCL.init(executorNum, rank, kvsIPPort)
val rankId = OneCCL.rankID()
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9 changes: 5 additions & 4 deletions mllib-dal/src/main/scala/org/apache/spark/ml/util/Utils.scala
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Expand Up @@ -33,8 +33,9 @@ object Utils {
val executorIPAddress = Utils.sparkFirstExecutorIP(data.sparkContext)
val kvsIP = data.sparkContext.conf.get("spark.oap.mllib.oneccl.kvs.ip",
executorIPAddress)
val kvsPortDetected = 5000
// val kvsPortDetected = Utils.checkExecutorAvailPort(data, kvsIP)
// TODO: right now we use a configured port, will optimize auto port detection
// val kvsPortDetected = Utils.checkExecutorAvailPort(data, kvsIP)
val kvsPortDetected = 3000
val kvsPort = data.sparkContext.conf.getInt("spark.oap.mllib.oneccl.kvs.port",
kvsPortDetected)

Expand Down Expand Up @@ -126,9 +127,9 @@ object Utils {
// check workers' platform compatibility
val executor_num = Utils.sparkExecutorNum(sc)
val data = sc.parallelize(1 to executor_num, executor_num)
val result = data.map { p =>
val result = data.mapPartitions { p =>
LibLoader.loadLibraries()
OneDAL.cCheckPlatformCompatibility()
Iterator(OneDAL.cCheckPlatformCompatibility())
}.collect()

result.forall(_ == true)
Expand Down