Real Time Machine Learning Visualization with Spark
Chester ChenDirector of EngineeringAlpine Data
May 3, 2016
COMPANY CONFIDENTIAL2
Who am I ?• Director of Engineering at Alpine Data• Founder and Organizer of SF Big Analytics Meetup (4000+
members)• Previous Employment:
– Symantec, AltaVista, Ascent Media, ClearStory Systems, WebWare. • Experience with Spark
– Exposed to Spark since Spark 0.6– Architect for Alpine Spark Integration on Spark 1.1, 1.3 and 1.5.x
• Hadoop Distribution– CDH, HDP and MapR
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Alpine Data at a Glance Enterprise Scale Predictive Analytics with deep experience in Machine Learning, Data Science, and Distributed Data Architectures
Industry Innovations and IPBroad patents awarded for in-cluster and in-database machine learning - 2012First web-based solution for end-to-end Predictive analytics - 2012Created Industry first integrated Analytics Services Platform - 2013First Predictive Analytics solution to be certified on Spark - 2014Launched Touchpoints, Industry first predictive applications service layer- 2015
Global Brand Names in Financial Services, Telco/Media, Healthcare, Manufacturing, Public Sector and RetailVisionary in the Gartner Magic Quadrant for Advanced Analytics
Key Partners:
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Lightning-fast cluster computing
Real Time ML Visualization with Spark
-- What is Spark
http://spark.apache.org/
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Iris data set, K-Means clustering with K=3Cluster 2
Cluster 1
Cluster 0
Centroids
Sepal width vs Petal length
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Iris data set, K-Means clustering with K=3
distance
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What is K-Means ?• Given a set of observations (x1, x2, …, xn), where each observation is a d-
dimensional real vector, • k-means clustering aims to partition the n observations into k (≤ n) sets
S = {S1, S2, …, Sk}• The clusters are determined by minimizing the inter-cluster sum of squares
(ICSS) (sum of distance functions of each point in the cluster to the K center). In other words, the objective is to find
• where μi is the mean of points in Si.• https://en.wikipedia.org/wiki/K-means_clustering
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Visualization Cost
0 5 10 15 20 2534
34.5
35
35.5
36
36.5
37
37.5
38
38.5
Cost vs Iteration
Cost
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Real Time ML Visualization – Why ?• Use Cases
– Use visualization to determine whether to end the training early• Need a way to visualize the training process including the
convergence, clustering or residual plots, etc. • Need a way to stop the training and save current model• Need a way to disable or enable the visualization
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Real Time ML Visualization with Spark
DEMO
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How to Enable Real Time ML Visualization ? • A callback interface for Spark Machine Learning Algorithm to send
messages – Algorithms decide when and what message to send– Algorithms don’t care how the message is delivered
• A task channel to handle the message delivery from Spark Driver to Spark Client– It doesn’t care about the content of the message or who sent the message
• The message is delivered from Spark Client to Browser– We use HTML5 Server-Sent Events ( SSE) and HTTP Chunked Response (PUSH) – Pull is possible, but requires a message Queue
• Visualization using JavaScript Frameworks Plot.ly and D3
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Spark Job in Yarn-Cluster mode
Spark Client
Hadoop Cluster
Yarn-ContainerSpark Driver
Spark Job
Spark Context
Spark ML algorithm
Command Line
Rest API
Servlet
Application Host
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Spark Job in Yarn-Cluster mode
Spark Client
Hadoop Cluster
Command Line
Rest API
Servlet
Application Host
Spark Job
App Context Spark ML Algorithms
ML Listener
Message Logger
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Spark Client
Hadoop Cluster
Application Host
Spark Job
App Context Spark ML Algorithms
ML Listener
Message Logger
Spark Job in Yarn-Cluster mode
Web/Rest API
Server
Akka
Browser
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Enable Real Time ML Visualization
SSE
PlotlyD3
Browser
Rest API
Server
Web Server
Spark Client
Hadoop Cluster
Spark Job
App Context
Message Logger
Task Channel
Spark ML Algorithms
ML Listener
AkkaChunked Response
Akka
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Enable Real Time ML Visualization
SSE
PlotlyD3
Browser
Rest API
Server
Web Server
Spark Client
Hadoop Cluster
Spark Job
App Context
Message Logger
Task Channel
Spark ML Algorithms
ML Listener
AkkaChunked Response
Akka
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Machine Learning Listeners
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Callback Interface: ML Listener
trait MLListener { def onMessage(message: => Any)}
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Callback Interface: MLListenerSupport trait MLListenerSupport {
// rest of codedef sendMessage(message: => Any): Unit = { if (enableListener) { listeners.foreach(l => l.onMessage(message)) }}
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KMeansEx: KMeans with MLListener
class KMeansExt private (…) extends Serializable with Logging with MLListenerSupport { ... }
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KMeansEx: KMeans with MLListenercase class KMeansCoreStats (iteration: Int, centers: Array[Vector], cost: Double )
private def runAlgorithm(data: RDD[VectorWithNorm]): KMeansModel = { ... while (!stopIteration && iteration < maxIterations && !activeRuns.isEmpty) {
...if (listenerEnabled()) {
sendMessage(KMeansCoreStats(…)) }...
}}
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KMeans ML Listener class KMeansListener(columnNames: List[String], data : RDD[Vector], logger : MessageLogger) extends MLListener{ var sampleDataOpt : Option[Array[Vector]]= None message match { case coreStats :KMeansCoreStats => if (sampleDataOpt.isEmpty)
sampleDataOpt = Some(data.takeSample(withReplacement = false, num=100)) //use the KMeans model of the current iteration to predict sample cluster indexes val kMeansModel = new KMeansModel(coreStats.centers) val cluster=sampleDataOpt.get.map(vector => (vector.toArray, kMeansModel.predict(vector)))
//construct message consists of sample, cost, iteration and centroids //use logger to send the message out }
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KMeans Spark Job Setupval kMeans = new KMeansExt().setK(numClusters) .setEpsilon(epsilon) .setMaxIterations(maxIterations) .enableListener(enableVisualization) .addListener( new KMeansListener(...))
appCtxOpt.foreach(_.addTaskObserver(new MLTaskObserver(kMeans,logger)))
kMeans.run(vectors)
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ML Task Observer• Receives command from User to update running Spark Job• Once receives UpdateTask Command from notify call, it
preforms the necessary update operation
trait TaskObserver { def notify (task: UpdateTaskCmd)}
class MLTaskObserver(support: MLListenerSupport, logger: MessageLogger ) extends TaskObserver { //implement notify }
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Logistic Regression MLListenerclass LogisticRegression(…) extends MLListenerSupport { def train(data: RDD[(Double, Vector)]): LogisticRegressionModel= {
// initialization code val (rawWeights, loss) = OWLQN.runOWLQN( …) generateLORModel(…) }
}
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Logistic Regression MLListenerobject OWLQN extends Logging { def runOWLQN(/*args*/,mlSupport:Option[MLListenerSupport]):(Vector, Array[Double]) = {
val costFun=new CostFun(data, mlSupport, IterationState(), /*other args */)val states : Iterator[lbfgs.State] = lbfgs.iterations(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector ) …}
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Logistic Regression MLListenerIn Cost function :
override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = {
val shouldStop = mlSupport.exists(_.stopIteration)
if (!shouldStop) { … mlSupport.filter(_.listenerEnabled()).map { s=> s.sendMessage( (iState.iteration, w, loss)) }
… } else { … }}
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Task Communication Channel
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Task Channel : Akka Messaging
Spark Application Application
Context
Actor System
MessagerActor
Task ChannelActor
SparkContext Spark tasks
Akka
Akka
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Task Channel : Akka messaging
SSE
PlotlyD3
Browser
Rest API
Server
Web Server
Spark Client
Hadoop Cluster
Spark Job
App Context
Message Logger
Task Channel
Spark ML Algorithms
ML Listener
AkkaChunked Response
Akka
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Push To The Browser
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HTTP Chunked Response and SSE
SSE
PlotlyD3
Browser
Rest API
Server
Web Server
Spark Client
Hadoop Cluster
Spark Job
App Context
Message Logger
Task Channel
Spark ML Algorithms
ML Listener
AkkaChunked Response
Akka
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HTML5 Server-Sent Events (SSE)• Server-sent Events (SSE) is one-way messaging
– An event is when a web page automatically get update from Server
• Register an event source (JavaScript) var source = new EventSource(url);• The Callback onMessage(data)
source.onmessage = function(message){...}• Data Format:
data: { \ndata: “key” : “value”, \n\ndata: } \n\n
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HTTP Chunked Response• Spray Rest Server supports Chunked Response
val responseStart = HttpResponse(entity = HttpEntity(`text/event-stream`, s"data: Start\n"))requestCtx.responder ! ChunkedResponseStart(responseStart).withAck(Messages.Ack)
val nextChunk = MessageChunk(s"data: $r \n\n")requestCtx.responder ! nextChunk.withAck(Messages.Ack)
requestCtx.responder ! MessageChunk(s"data: Finished \n\n")requestCtx.responder ! ChunkedMessageEnd
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Push vs. PullPush• Pros
– The data is streamed (pushed) to browser via chunked response
– There is no need for data queue, but the data can be lost if not consumed
– Multiple pages can be pushed at the same time, which allows multiple visualization views
• Cons– For slow network, slow browser and fast data iterations, the
data might all show-up in browser at once, rather showing a nice iteration-by-iteration display
– If you control the data chunked response by Network Acknowledgement, the visualization may not show-up at all as the data is not pushed due to slow network acknowledgement
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Push vs. PullPull• Pros
– Message does not get lost, since it can be temporarily stored in the message queue
– The visualization will render in an even pace • Cons
– Need to periodically send server request for update,– We will need a message queue before the message is
consumed– Hard to support multiple pages rendering with simple
message queue
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Visualization: Plot.ly + D3
Cost vs. IterationCost vs. Iteration
ArrTime vs. DistanceArrTime vs. DepTime
Alpine Workflow
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Use Plot.ly to render graph
function showCost(dataParsed) { var costTrace = { … }; var data = [ costTrace ]; var costLayout = { xaxis: {…}, yaxis: {…}, title: … }; Plotly.newPlot('cost', data, costLayout);}
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Real Time ML Visualization: Summary• Training machine learning model involves a lot of
experimentation, we need a way to visualize the training process.
• We presented a system to enable real time machine learning visualization with Spark: – Gives visibility into the training of a model– Allows us monitor the convergence of the algorithms during
training– Can stop the iterations when convergence is good enough.
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Thank YouChester Chen [email protected]
LinkedInhttps://www.linkedin.com/in/chester-chen-3205992
SlideSharehttp://www.slideshare.net/ChesterChen/presentations
demo videohttps://youtu.be/DkbYNYQhrao
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