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https://issues.apache.org/jira/browse/SPARK-6932?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14529826#comment-14529826
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Zhenhong Chen commented on SPARK-6932:
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I appreciate investigating the PS on Spark. However, it seems much complicated
when dealing with distributed model and asynchronous update comparing to a
external PS service. As for failed task, we can avoid returning finished
iterations by asking the PS clock via the client. In this way, the training
procedure can be done in only one mapPartition.
> A Prototype of Parameter Server
> -------------------------------
>
> Key: SPARK-6932
> URL: https://issues.apache.org/jira/browse/SPARK-6932
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib, Spark Core
> Reporter: Qiping Li
>
> h2. Introduction
> As specified in
> [SPARK-4590|https://issues.apache.org/jira/browse/SPARK-4590],it would be
> very helpful to integrate parameter server into Spark for machine learning
> algorithms, especially for those with ultra high dimensions features.
> After carefully studying the design doc of [Parameter
> Servers|https://docs.google.com/document/d/1SX3nkmF41wFXAAIr9BgqvrHSS5mW362fJ7roBXJm06o/edit?usp=sharing],and
> the paper of [Factorbird|http://stanford.edu/~rezab/papers/factorbird.pdf],
> we proposed a prototype of Parameter Server on Spark(Ps-on-Spark), with
> several key design concerns:
> * *User friendly interface*
> Careful investigation is done to most existing Parameter Server
> systems(including: [petuum|http://petuum.github.io], [parameter
> server|http://parameterserver.org],
> [paracel|https://github.com/douban/paracel]) and a user friendly interface is
> design by absorbing essence from all these system.
> * *Prototype of distributed array*
> IndexRDD (see
> [SPARK-4590|https://issues.apache.org/jira/browse/SPARK-4590]) doesn't seem
> to be a good option for distributed array, because in most case, the #key
> updates/second is not be very high.
> So we implement a distributed HashMap to store the parameters, which can
> be easily extended to get better performance.
>
> * *Minimal code change*
> Quite a lot of effort in done to avoid code change of Spark core. Tasks
> which need parameter server are still created and scheduled by Spark's
> scheduler. Tasks communicate with parameter server with a client object,
> through *akka* or *netty*.
> With all these concerns we propose the following architecture:
> h2. Architecture
> !https://cloud.githubusercontent.com/assets/1285855/7158179/f2d25cc4-e3a9-11e4-835e-89681596c478.jpg!
> Data is stored in RDD and is partitioned across workers. During each
> iteration, each worker gets parameters from parameter server then computes
> new parameters based on old parameters and data in the partition. Finally
> each worker updates parameters to parameter server.Worker communicates with
> parameter server through a parameter server client,which is initialized in
> `TaskContext` of this worker.
> The current implementation is based on YARN cluster mode,
> but it should not be a problem to transplanted it to other modes.
> h3. Interface
> We refer to existing parameter server systems(petuum, parameter server,
> paracel) when design the interface of parameter server.
> *`PSClient` provides the following interface for workers to use:*
> {code}
> // get parameter indexed by key from parameter server
> def get[T](key: String): T
> // get multiple parameters from parameter server
> def multiGet[T](keys: Array[String]): Array[T]
> // add parameter indexed by `key` by `delta`,
> // if multiple `delta` to update on the same parameter,
> // use `reduceFunc` to reduce these `delta`s frist.
> def update[T](key: String, delta: T, reduceFunc: (T, T) => T): Unit
> // update multiple parameters at the same time, use the same `reduceFunc`.
> def multiUpdate(keys: Array[String], delta: Array[T], reduceFunc: (T, T) =>
> T: Unit
>
> // advance clock to indicate that current iteration is finished.
> def clock(): Unit
>
> // block until all workers have reached this line of code.
> def sync(): Unit
> {code}
> *`PSContext` provides following functions to use on driver:*
> {code}
> // load parameters from existing rdd.
> def loadPSModel[T](model: RDD[String, T])
> // fetch parameters from parameter server to construct model.
> def fetchPSModel[T](keys: Array[String]): Array[T]
> {code}
>
> *A new function has been add to `RDD` to run parameter server tasks:*
> {code}
> // run the provided `func` on each partition of this RDD.
> // This function can use data of this partition(the first argument)
> // and a parameter server client(the second argument).
> // See the following Logistic Regression for an example.
> def runWithPS[U: ClassTag](func: (Array[T], PSClient) => U): Array[U]
>
> {code}
> h2. Example
> Here is an example of using our prototype to implement logistic regression:
> {code:title=LogisticRegression.scala|borderStyle=solid}
> def train(
> sc: SparkContext,
> input: RDD[LabeledPoint],
> numIterations: Int,
> stepSize: Double,
> miniBatchFraction: Double): LogisticRegressionModel = {
>
> // initialize weights
> val numFeatures = input.map(_.features.size).first()
> val initialWeights = new Array[Double](numFeatures)
> // initialize parameter server context
> val pssc = new PSContext(sc)
> // load initialized weights into parameter server
> val initialModelRDD = sc.parallelize(Array(("w", initialWeights)), 1)
> pssc.loadPSModel(initialModelRDD)
> // run logistic regression algorithm on input data
> input.runWithPS((arr, client) => {
> val sampler = new BernoulliSampler[LabeledPoint](miniBatchFraction)
>
> // for each iteration, compute delta and update weights
> for (i <- 0 to numIterations) {
> // get weights from parameter server
> val weights = Vectors.dense(client.get[Array[Double]]("w"))
> sampler.setSeed(i + 42)
> // for each sample point, compute delta and update weights
> sampler.sample(arr.toIterator).foreach { point =>
> // compute delta
> val data = point.features
> val label = point.label
> val margin = -1.0 * dot(data, weights)
> val multiplier = (1.0 / (1.0 + math.exp(margin))) - label
> val delta = Vectors.dense(new Array[Double](numFeatures))
> axpy((-1) * stepSize / math.sqrt(i + 1) * multiplier, data, delta)
> // update weights
> client.update("w", delta.toArray, (d1, d2) => {
> d1.zip(d2).map((a, b) => a + b)
> })
> }
>
> // end of current iteration
> client.clock()
> }
> })
> // fetch weights from parameter server
> val weights =
> Vectors.dense(pssc.fetchPSModel[Array[Double]](Array("w"))(0))
> val intercept = 0.0
> // construct LogisiticRegressionModel
> new LogisticRegressionModel(weights, intercept).clearThreshold()
> }
> {code}
> The above code can be run on current PS-on-Spark implementation.
> h2. Other considerations
> The current implementation is just a prototype and we will try to improve it
> in the following directions:
> h3. Consistency protocol
> Currently we have just implemented BSP protocol. And SSP consistency will be
> added soon.
> h3. Model partition across servers
> Currently all the parameters are stored on a single server. Parameters should
> be partitioned across multiple servers when the parameter size get large.
> Parameter server client should route request to different servers
> accordingly.
> h3. Performance optimizing
> To get better performance, client can cache parameter servers and store
> updates through operation log(as petuum does). There may be some other ways
> to improve performance.
> h3. Fault Recovery
> When a parameter server crashes, it should be restarted on another node. Data
> of a parameter server should be periodically checkpointed so it can be
> transfered when a server is restarted.When a task is restarted, it should not
> rerun finished iterations.
> We would like to see parameter server integrated into Spark soon and hope
> this help other Spark users who need parameter server. As specified above,
> there is still much work to be done so any comments are welcome.
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