[
https://issues.apache.org/jira/browse/SPARK-6697?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Guoqiang Li updated SPARK-6697:
-------------------------------
Description:
When I run this [branch(lrGraphxSGD)|
https://github.com/witgo/spark/tree/lrGraphxSGD] .
PeriodicGraphCheckpointer only clear the vertices.
{code}
def run(iterations: Int): Unit = {
for (iter <- 1 to iterations) {
logInfo(s"Start train (Iteration $iter/$iterations)")
val margin = forward()
margin.setName(s"margin-$iter").persist(storageLevel)
println(s"train (Iteration $iter/$iterations) cost : ${error(margin)}")
var gradient = backward(margin)
gradient = updateDeltaSum(gradient, iter)
dataSet = updateWeight(gradient, iter)
dataSet.vertices.setName(s"vertices-$iter")
dataSet.edges.setName(s"edges-$iter")
dataSet.persist(storageLevel)
graphCheckpointer.updateGraph(dataSet)
margin.unpersist(blocking = false)
gradient.unpersist(blocking = false)
logInfo(s"End train (Iteration $iter/$iterations)")
innerIter += 1
}
graphCheckpointer.deleteAllCheckpoints()
}
// Updater for L1 regularized problems
private def updateWeight(delta: VertexRDD[Double], iter: Int): Graph[VD, ED]
= {
val thisIterStepSize = if (useAdaGrad) stepSize else stepSize / sqrt(iter)
val thisIterL1StepSize = stepSize / sqrt(iter)
val newVertices = dataSet.vertices.leftJoin(delta) { (_, attr, gradient) =>
gradient match {
case Some(gard) => {
var weight = attr
weight -= thisIterStepSize * gard
if (regParam > 0.0 && weight != 0.0) {
val shrinkageVal = regParam * thisIterL1StepSize
weight = signum(weight) * max(0.0, abs(weight) - shrinkageVal)
}
assert(!weight.isNaN)
weight
}
case None => attr
}
}
GraphImpl(newVertices, dataSet.edges)
}
{code}
was:
When I run this [branch(lrGraphxSGD)|
https://github.com/witgo/spark/tree/lrGraphxSGD] .
PeriodicGraphCheckpointer only clear the vertices.
{code}
def run(iterations: Int): Unit = {
for (iter <- 1 to iterations) {
logInfo(s"Start train (Iteration $iter/$iterations)")
val margin = forward()
margin.setName(s"margin-$iter").persist(storageLevel)
println(s"train (Iteration $iter/$iterations) cost : ${error(margin)}")
var gradient = backward(margin)
gradient = updateDeltaSum(gradient, iter)
dataSet = updateWeight(gradient, iter)
dataSet.vertices.setName(s"vertices-$iter")
dataSet.edges.setName(s"edges-$iter")
dataSet.persist(storageLevel)
graphCheckpointer.updateGraph(dataSet)
margin.unpersist(blocking = false)
gradient.unpersist(blocking = false)
logInfo(s"End train (Iteration $iter/$iterations)")
innerIter += 1
}
graphCheckpointer.deleteAllCheckpoints()
}
{code}
> PeriodicGraphCheckpointer is not clear Edges.
> ---------------------------------------------
>
> Key: SPARK-6697
> URL: https://issues.apache.org/jira/browse/SPARK-6697
> Project: Spark
> Issue Type: Bug
> Components: GraphX, MLlib
> Affects Versions: 1.3.0
> Reporter: Guoqiang Li
> Attachments: QQ20150403-1.png
>
>
> When I run this [branch(lrGraphxSGD)|
> https://github.com/witgo/spark/tree/lrGraphxSGD] .
> PeriodicGraphCheckpointer only clear the vertices.
> {code}
> def run(iterations: Int): Unit = {
> for (iter <- 1 to iterations) {
> logInfo(s"Start train (Iteration $iter/$iterations)")
> val margin = forward()
> margin.setName(s"margin-$iter").persist(storageLevel)
> println(s"train (Iteration $iter/$iterations) cost : ${error(margin)}")
> var gradient = backward(margin)
> gradient = updateDeltaSum(gradient, iter)
> dataSet = updateWeight(gradient, iter)
> dataSet.vertices.setName(s"vertices-$iter")
> dataSet.edges.setName(s"edges-$iter")
> dataSet.persist(storageLevel)
> graphCheckpointer.updateGraph(dataSet)
> margin.unpersist(blocking = false)
> gradient.unpersist(blocking = false)
> logInfo(s"End train (Iteration $iter/$iterations)")
> innerIter += 1
> }
> graphCheckpointer.deleteAllCheckpoints()
> }
> // Updater for L1 regularized problems
> private def updateWeight(delta: VertexRDD[Double], iter: Int): Graph[VD,
> ED] = {
> val thisIterStepSize = if (useAdaGrad) stepSize else stepSize / sqrt(iter)
> val thisIterL1StepSize = stepSize / sqrt(iter)
> val newVertices = dataSet.vertices.leftJoin(delta) { (_, attr, gradient)
> =>
> gradient match {
> case Some(gard) => {
> var weight = attr
> weight -= thisIterStepSize * gard
> if (regParam > 0.0 && weight != 0.0) {
> val shrinkageVal = regParam * thisIterL1StepSize
> weight = signum(weight) * max(0.0, abs(weight) - shrinkageVal)
> }
> assert(!weight.isNaN)
> weight
> }
> case None => attr
> }
> }
> GraphImpl(newVertices, dataSet.edges)
> }
> {code}
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