Download as PDF, PPTX
![Sample Code
source("nn/layers/affine.dml") as affine # import a file in the “affine“ namespace
[W, b] = affine::init(D, M) # calls the init function, multiple
return
parfor (i in 1:nrow(X)) { # i iterates over 1 through num rows in X in parallel
for (j in 1:ncol(X)) { # j iterates over 1 through num cols in X
# Computation ...
}
}
write (M, fileM, format=“text”) # M=matrix, fileM=file, also writes to
HDFS
X = read (fileX) # fileX=file, also reads from HDFS
if (ncol (A) > 1) {
# Matrix A is being sliced by a given range of columns
A[,1:(ncol (A) - 1)] = A[,1:(ncol (A) - 1)] - A[,2:ncol (A)];
}
11](https://image.slidesharecdn.com/s1scalablemachinelearningwithapachesystemml-160913175958/75/Overview-of-Apache-SystemML-by-Berthold-Reinwald-and-Nakul-Jindal-11-2048.jpg)
![Sample Code
interpSpline = function(
double x, matrix[double] X, matrix[double] Y, matrix[double] K) return (double q) {
i = as.integer(nrow(X) - sum(ppred(X, x, ">=")) + 1)
# misc computation …
q = as.scalar(qm)
}
eigen = externalFunction(Matrix[Double] A)
return(Matrix[Double] eval, Matrix[Double] evec)
implemented in (classname="org.apache.sysml.udf.lib.EigenWrapper",
exectype="mem")
12](https://image.slidesharecdn.com/s1scalablemachinelearningwithapachesystemml-160913175958/75/Overview-of-Apache-SystemML-by-Berthold-Reinwald-and-Nakul-Jindal-12-2048.jpg)
![Sample Code (From LinearRegDS.dml*)
A = t(X) %*% X
b = t(X) %*% y
if (intercept_status == 2) {
A = t(diag (scale_X) %*% A + shift_X %*% A [m_ext, ])
A = diag (scale_X) %*% A + shift_X %*% A [m_ext, ]
b = diag (scale_X) %*% b + shift_X %*% b [m_ext, ]
}
A = A + diag (lambda)
print ("Calling the Direct Solver...")
beta_unscaled = solve (A, b)
*https://github.com/apache/incubator-systemml/blob/master/scripts/algorithms/LinearRegDS.dml#L133
13](https://image.slidesharecdn.com/s1scalablemachinelearningwithapachesystemml-160913175958/75/Overview-of-Apache-SystemML-by-Berthold-Reinwald-and-Nakul-Jindal-13-2048.jpg)
![End-to-end on Spark … in Code
22
import org.apache.spark.sql._
val ctx = new org.apache.spark.sql.SQLContext(sc)
val tweets = ctx.jsonFile("hdfs:/twitter/decahose")
tweets.registerAsTable("tweetTable")
ctx.sql("SELECT text FROM tweetTable LIMIT 5").collect.foreach(println)
ctx.sql("SELECT lang, COUNT(*) AS cnt FROM tweetTable
GROUP BY lang ORDER BY cnt DESC LIMIT 10").collect.foreach(println)
val texts = ctx.sql("SELECT text FROM tweetTable").map(_.head.toString)
def featurize(str: String): Vector = { ... }
val vectors = texts.map(featurize).toDF.cache()
val mcV = new MatrixCharacteristics(vectors.count, vocabSize, 1000,1000)
val V = RDDConvertUtilsExt(sc, vectors, mcV, false, "_1")
val ml = new com.ibm.bi.dml.api.MLContext(sc)
ml.registerInput("V", V, mcV)
ml.registerOutput("W")
ml.registerOutput("H")
val args = Array(numTopics, numGNMFIter)
val out = ml.execute("GNMF.dml", args)
val W = out.getDF("W")
val H = out.getDF("H")
def getWords(r: Row): Array[(String, Double)] = { ... }
val topics = H.rdd.map(getWords)
Twitter Data
Explore Data
In SQL
Data Set
Training Set
Topic Modeling
SQLML
Get Topics](https://image.slidesharecdn.com/s1scalablemachinelearningwithapachesystemml-160913175958/75/Overview-of-Apache-SystemML-by-Berthold-Reinwald-and-Nakul-Jindal-22-2048.jpg)
![Selected Algebraic Simplification
Rewrites
25
Name Dynamic Pattern
Remove Unnecessary Indexing X[a:b,c:d] = Y à X = Y iff dims(X)=dims(Y)
X = Y[, 1] à X = Y iff ncol(Y)=1
Remove Empty
Matrix Multiply
X%*%Y à matrix(0,nrow(X),ncol(Y))
iff nnz(X)=0|nnz(Y)=0
Removed Unnecessary Outer
Product
X*(Y%*%matrix(1,...)) à X*Y
iff ncol(Y)=1
Simplify Diag Aggregates sum(diag(X))àtrace(X) iff ncol(X)=1
SimplifyMatrix Mult Diag diag(X)%*%Y à X*Y iff ncol(X)=1&ncol(Y)=1
Simplify Diag Matrix Mult diag(X%*%Y) à rowSums(X*t(Y)) iff ncol(Y)>1
Simplify Dot Product Sum sum(X^2) à t(X)%*%X iff ncol(X)=1
Name Static Pattern
Remove Unnecessary Operations t(t(X)), X/1, X*1, X-0 à X matrix(1,)/X à 1/X
rand(,min=-1,max=1)*7 à rand(,min=-7,max=7)
Binary to Unary X+X à 2*X X*X à X^2 X-X*Y à X*(1-Y)
Simplify Diag Aggregates trace(X%*%Y)àsum(X*t(Y))](https://image.slidesharecdn.com/s1scalablemachinelearningwithapachesystemml-160913175958/75/Overview-of-Apache-SystemML-by-Berthold-Reinwald-and-Nakul-Jindal-25-2048.jpg)
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Similar to Overview of Apache SystemML by Berthold Reinwald and Nakul Jindal
Overview of Apache SystemML by Berthold Reinwald and Nakul Jindal
- 1.
- 2. Agenda • What is Apache SystemML • How to implement SystemMLalgorithms è data scientist • How to run SystemML algorithms è user • How does SystemML work è SystemML developer 2
- 3. What is Apache SystemML • In a nutshell • a language for data scientists to implement scalable ML algorithms •2 language variants: R-like and Python-like syntax • Strong foundation of linear algebra operations and statistical functions • Comes with approx. 20+ algorithms pre-implemented • Cost-based optimizer to compile execution plans • Depending on data characteristics (tall/skinny, short/wide; dense/sparse) and cluster characteristics • ranging from single node to clusters (MapReduce, Spark); hybrid plans • APIs & Tools • Command line: hadoop jar, spark-submit, standalone Java app • JMLC: embed as library • Spark MLContext: Scala, Python, and Java • Tools • REPL (Scala Spark and pyspark) • Spark ML pipeline 3
- 4. Big Data Analytics - Characteristics • Large number of models •Large number of data points • Large number of features • Sparse data • Large number/size of intermediates • Large number of pairs • Custom analytics 4
- 5. SystemML – Declarative ML • Analytics language for data scientists (“The SQL for analytics”) •Algorithms expressed in a declarative, high-level language DML with R-like syntax • Productivity of data scientists • Enable • Solutions development • Tools • Compiler • Cost-based optimizer to generate execution plans and to parallelize • based on data characteristics • based on cluster and machine characteristics • Physical operators for in-memory single node and cluster execution • Performance & Scalability 5
- 6. High-Level SystemML Architecture 6 Hadoop or SparkCluster (scale-out) In-Memory Single Node (scale-up) Runtime Compiler Language DML Scripts DML (Declarative Machine Learning Language)
- 7. Apache SystemML Incubator Project • June, 2015: SystemMLopen source announced at Spark Summit • Sep., 2015: public github • Oct., 2015: 1st open source binary release (0.8.0) • Nov., 2015: Enter Apache incubation • http://systemml.apache.org/ • https://github.com/apache/incubator-systemml • Jan., 2016: SystemML 0.9.0 (1st Apache release) • June, 2016: SystemML 0.10.0 release 7
- 8. Apache SystemML Incubator http://systemml.apache.org/ • Get SystemML • Documentation •DML Reference Guide • Algorithms Guide • Running • Community • JIRA server • GitHub 8
- 9.
- 10. Sample Code A = 1.0# A is an integer X <- matrix(“4 3 2 5 7 8”, rows=3, cols=2) # X = matrix of size 3,2 '<-' is assignment Y = matrix(1, rows=3, cols=2) # Y = matrix of size 3,2 with all 1s b <- t(X) %*% Y # %*% is matrix multiply, t(X) is transpose S = "hello world" i=0 while(i < max_iteration) { H = (H * (t(W) %*% (V/(W%*%H))))/t(colSums(W)) # * is element by element mult W = (W * ((V/(W%*%H)) %*% t(H)))/t(rowSums(H)) i = i + 1; # i is an integer } print (toString(H)) # toString converts a matrix to a string 10
- 11. Sample Code source("nn/layers/affine.dml") as affine# import a file in the “affine“ namespace [W, b] = affine::init(D, M) # calls the init function, multiple return parfor (i in 1:nrow(X)) { # i iterates over 1 through num rows in X in parallel for (j in 1:ncol(X)) { # j iterates over 1 through num cols in X # Computation ... } } write (M, fileM, format=“text”) # M=matrix, fileM=file, also writes to HDFS X = read (fileX) # fileX=file, also reads from HDFS if (ncol (A) > 1) { # Matrix A is being sliced by a given range of columns A[,1:(ncol (A) - 1)] = A[,1:(ncol (A) - 1)] - A[,2:ncol (A)]; } 11
- 12. Sample Code interpSpline = function( doublex, matrix[double] X, matrix[double] Y, matrix[double] K) return (double q) { i = as.integer(nrow(X) - sum(ppred(X, x, ">=")) + 1) # misc computation … q = as.scalar(qm) } eigen = externalFunction(Matrix[Double] A) return(Matrix[Double] eval, Matrix[Double] evec) implemented in (classname="org.apache.sysml.udf.lib.EigenWrapper", exectype="mem") 12
- 13. Sample Code (From LinearRegDS.dml*) A = t(X)%*% X b = t(X) %*% y if (intercept_status == 2) { A = t(diag (scale_X) %*% A + shift_X %*% A [m_ext, ]) A = diag (scale_X) %*% A + shift_X %*% A [m_ext, ] b = diag (scale_X) %*% b + shift_X %*% b [m_ext, ] } A = A + diag (lambda) print ("Calling the Direct Solver...") beta_unscaled = solve (A, b) *https://github.com/apache/incubator-systemml/blob/master/scripts/algorithms/LinearRegDS.dml#L133 13
- 14. DML Editor Support • Very rudimentary editor support • Bit of shameless self-promotion : • Atom – Hackable Text editor • Install package - https://atom.io/packages/language-dml • From GUI - http://flight-manual.atom.io/using-atom/sections/atom-packages/ • Or from command line – apm install language-dml • Rudimentary snippet based completion of builtin function • Vim • Install package - https://github.com/nakul02/vim-dml • Works with Vundle (vim package manager) • There is an experimental Zeppelin Notebook integration with DML – • https://issues.apache.org/jira/browse/SYSTEMML-542 • Available as a docker image to play with - https://hub.docker.com/r/nakul02/incubator- zeppelin/ • Please send feedback when using these, requests for features, bugs • I’ll work on them when I can 14
- 15. SystemML Algorithms 15 Category Description DescriptiveStatistics Univariate Bivariate Stratified Bivariate Classification Logistic Regression (multinomial) Multi-Class SVM Naïve Bayes (multinomial) Decision Trees Random Forest Clustering k-Means Regression Linear Regression system of equations CG (conjugate gradient descent) Generalized Linear Models (GLM) Distributions: Gaussian, Poisson, Gamma, InverseGaussian, Binomial, Bernoulli Links for all distributions: identity, log, sq. root,inverse, 1/μ2 Links for Binomial / Bernoulli: logit, probit, cloglog, cauchit Stepwise Linear GLM Dimension Reduction PCA Matrix Factorization ALS direct solve CG (conjugate gradient descent) Survival Models Kaplan Meier Estimate Cox Proportional Hazard Regression Predict Algorithm-specific scoring Transformation (native) Recoding, dummy coding, binning, scaling, missing value imputation Documentation: https://apache.github.io/incubator-systemml/algorithms-reference.html Scripts: /usr/SystemML/systemml-0.10.0-incubating/scripts/algorithms/
- 16. Running / Invoking SystemML • Command line • Standalone (Java application in single JVM, in bin folder) •Spark (spark-submit, in scripts folder) • hadoop command line • APIs (MLContext) • Scala, e.g. run from Spark shell • Python, e.g. run from PySpark • Java • In-Memory 16
- 17. MLContext API – Example Usage val ml= new MLContext(sc) val X_train = sc.textFile("amazon0601.txt") .filter(!_.startsWith("#")) .map(_.split("t") match{case Array(prod1, prod2)=>(prod1.toInt, prod2.toInt,1.0)}) .toDF("prod_i", "prod_j", "x_ij") .filter("prod_i < 5000 AND prod_j < 5000") // Change to smaller number .cache() 17
- 18. MLContext API – Example Usage valpnmf = """ # data & args X = read($X) rank = as.integer($rank) # Computation .... write(negloglik, $negloglikout) write(W, $Wout) write(H, $Hout) """ 18
- 19. MLContext API – Example Usage valpnmf = """ # data & args X = read($X) rank = as.integer($rank) # Computation .... write(negloglik, $negloglikout) write(W, $Wout) write(H, $Hout) """ ml.registerInput("X", X_train) ml.registerOutput("W") ml.registerOutput("H") ml.registerOutput("negloglik") val outputs = ml.executeScript(pnmf, Map("maxiter" -> "100", "rank" -> "10")) val negloglik = getScalarDouble(outputs, "negloglik") 19
- 20.
- 21.
- 22. End-to-end on Spark … in Code 22 import org.apache.spark.sql._ val ctx= new org.apache.spark.sql.SQLContext(sc) val tweets = ctx.jsonFile("hdfs:/twitter/decahose") tweets.registerAsTable("tweetTable") ctx.sql("SELECT text FROM tweetTable LIMIT 5").collect.foreach(println) ctx.sql("SELECT lang, COUNT(*) AS cnt FROM tweetTable GROUP BY lang ORDER BY cnt DESC LIMIT 10").collect.foreach(println) val texts = ctx.sql("SELECT text FROM tweetTable").map(_.head.toString) def featurize(str: String): Vector = { ... } val vectors = texts.map(featurize).toDF.cache() val mcV = new MatrixCharacteristics(vectors.count, vocabSize, 1000,1000) val V = RDDConvertUtilsExt(sc, vectors, mcV, false, "_1") val ml = new com.ibm.bi.dml.api.MLContext(sc) ml.registerInput("V", V, mcV) ml.registerOutput("W") ml.registerOutput("H") val args = Array(numTopics, numGNMFIter) val out = ml.execute("GNMF.dml", args) val W = out.getDF("W") val H = out.getDF("H") def getWords(r: Row): Array[(String, Double)] = { ... } val topics = H.rdd.map(getWords) Twitter Data Explore Data In SQL Data Set Training Set Topic Modeling SQLML Get Topics
- 23. SystemML Architecture Language • R- likesyntax • Linear algebra, statisticalfunctions, controlstructures, etc. • User-defined & externalfunction • Parsing • Statement blocks & statements • Program Analysis, type inference, dead code elimination High-Level Operator (HOP) Component • Dataflow in DAGs of operations on matrices, frames, and scalars • Choosing from alternative execution plans based on memoryand cost estimates: operatorordering & selection; hybrid plans Low-Level Operator (LOP) Component • Low-levelphysicalexecution plan (LOPDags)overkey-value pairs • “Piggybacking”operationsinto minimalnumber Map-Reduce jobs Runtime • Hybrid Runtime • CP: single machine operations & orchestrate jobs • MR: generic Map-Reduce jobs & operations • SP: Spark Jobs • Numerically stable operators • Dense / sparse matrix representation • Multi-Levelbuffer pool (caching) to evict in-memory objects • Dynamic Recompilation for initial unknowns Command Line JMLC Spark MLContext APIs High-Level Operators Parser/Language Low-Level Operators Compiler Runtime Control Program Runtime Program Buffer Pool ParFor Optimizer/ Runtime MR InstSpark Inst CP Inst Recompiler Cost-based optimizations DFS IOMem/FS IO Generic MR Jobs MatrixBlock Library (single/multi-threaded) 23
- 24. SystemML Compilation Chain 24 CP + bsb _mVar1 SPARK mapmm X.MATRIX.DOUBLE _mvar1.MATRIX.DOUBLE _mVar2.MATRIX.DOUBLE RIGHT false NONE CP * y _mVar2 _mVar3
- 25. Selected Algebraic Simplification Rewrites 25 Name Dynamic Pattern Remove Unnecessary Indexing X[a:b,c:d]= Y à X = Y iff dims(X)=dims(Y) X = Y[, 1] à X = Y iff ncol(Y)=1 Remove Empty Matrix Multiply X%*%Y à matrix(0,nrow(X),ncol(Y)) iff nnz(X)=0|nnz(Y)=0 Removed Unnecessary Outer Product X*(Y%*%matrix(1,...)) à X*Y iff ncol(Y)=1 Simplify Diag Aggregates sum(diag(X))àtrace(X) iff ncol(X)=1 SimplifyMatrix Mult Diag diag(X)%*%Y à X*Y iff ncol(X)=1&ncol(Y)=1 Simplify Diag Matrix Mult diag(X%*%Y) à rowSums(X*t(Y)) iff ncol(Y)>1 Simplify Dot Product Sum sum(X^2) à t(X)%*%X iff ncol(X)=1 Name Static Pattern Remove Unnecessary Operations t(t(X)), X/1, X*1, X-0 à X matrix(1,)/X à 1/X rand(,min=-1,max=1)*7 à rand(,min=-7,max=7) Binary to Unary X+X à 2*X X*X à X^2 X-X*Y à X*(1-Y) Simplify Diag Aggregates trace(X%*%Y)àsum(X*t(Y))
- 26. A Data Scientist – Linear Regression 26 X ≈ Explanatory/ IndependentVariables Predicted/ Dependant VariableModel w w = argminw ||Xw-y||2 +λ||w||2 Optimization Problem: next direction Iterate until convergence initialize step size update w initial direction accuracy measures Conjugate GradientMethod: • Start off with the (negative) gradient • For each step 1. Move to the optimal point along the chosen direction; 2. Recompute the gradient; 3. Project it onto the subspace conjugate* to allprior directions; 4. Use this as the next direction (* conjugate =orthogonalgiven A as the metric) A = XT X + λ y
- 27. SystemML – Run LinRegCG on Spark 27 100M 10,000 100M 1 yX 100M 1,000 X 100M 100 X 100M 10 X 100M 1 y 100M 1 y 100M 1 y 8 TB 800 GB 80 GB 8 GB … tMMp … Multithreaded Single Node 20 GB Driver on 16c 6 x 55 GB Executors Hybrid Plan with RDD caching and fused operator Hybrid Plan with RDD out-of- core and fused operator Hybrid Plan with RDD out-of- core and different operators … x.persist(); ... X.mapValues(tMMp ) .reduce () … Driver Fused Executors … RDD cache: X tMMv tMMv … x.persist(); ... X.mapValues(tMMp) .reduce() ... Executors … RDD cache: X tMMv tMMv Driver Spilling … x.persist(); ... // 2 MxV mult // with broadcast, // mapToPair, and // reduceByKey ... Executors … RDD cache: X Mv tvM Mv tvM Driver Driver Cache
- 28. LinReg CG for varying Data 28 8 GB 100Mx 10 80 GB 100M x 100 800 GB 100M x 1K 8 TB 100M x 10K CP+Spark 21 92 2,065 40,395 Spark 76 124 2,159 40,130 CP+MR 24 277 2,613 41,006 10 100 1,000 10,000 100,000 ExecutionTimeinsecs(logscale) Data Size Note Driver w+h 20 GB, 16c 6 Executors each 55 GB, 24c Convergence in 3-4 itera+ons SystemML as of 10/2015 Single node MT avoids Spark Ctx & distributed ops 3.6 x Hybrid plan & RDD caching 3x Out of Core 1.2x Fully Utilized Ø Cost-based optimization is important Ø Hybrid execution plans benefit especially medium- sized data sets Ø Aggregated in-memory data sets are sweet spot for Spark esp. for iterative algorithms Ø Graceful degradation for out-of-core
- 29. Apache SystemML - Summary • Cost-based compilation of machine learning algorithms generates execution plans •for single-node in-memory, cluster, and hybrid execution • for varying data characteristics: • varying number of observations (1,000s to 10s of billions) • varying number of variables (10s to 10s of millions) • dense and sparse data • for varying cluster characteristics (memory configurations, degree of parallelism) • Out-of-the-box, scalable machine learning algorithms • e.g. descriptive statistics, regression, clustering, and classification • "Roll-your-own" algorithms • Enable programmer productivity (no worry about scalability, numeric stability, and optimizations) • Fast turn-around for new algorithms • Higher-level language shields algorithm development investment from platform progression • Yarn for resource negotiation and elasticity • Spark for in-memory, iterative processing 29
- 30. Roadmap • Algorithms • kNN, word2vec, non-linear SVM, etc. •Deep learning • Engine • Compressed Linear Algebra • Code Gen • Extensions for Deep Learning • GPU backend • Usability • DML notebook • Language integration • API cleanup 30
- 31. Research Papers • Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large Scale Machine Learning. Conditional Accept at VLDB 2016 • Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, FarazMakari Manshadi, NiketanPansare, Berthold Reinwald, Frederick R. Reiss, PrithvirajSen, Arvind C. Surve, Shirish Tatikonda. SystemML: Declarative Machine Learning on Spark. VLDB 2016 • Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss: Resource Elasticity for Large-Scale Machine Learning. SIGMOD Conference 2015:137-152 • Arash Ashari,Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan: On optimizing machine learning workloads via kernel fusion. PPOPP 2015:173-182 • Sebastian Schelter, Juan Soto, Volker Markl, Douglas Burdick, Berthold Reinwald, Alexandre V. Evfimievski: Efficient sample generation for scalable meta learning. ICDE 2015:1191-1202 • Matthias Boehm, Douglas R. Burdick,Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, PrithvirajSen, Shirish Tatikonda, Yuanyuan Tian: SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. IEEE Data Eng. Bull. 37(3):52-62 (2014) • Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, PrithvirajSen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan: Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. PVLDB 7(7): 553-564 (2014) • Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem: Large Scale Discriminative Metric Learning. IPDPS Workshops2014:1656-1663 • Yuanyuan Tian, Shirish Tatikonda, Berthold Reinwald: Scalable and Numerically Stable Descriptive Statistics in SystemML. ICDE 2012:1351-1359 • Amol Ghoting, Rajasekar Krishnamurthy,Edwin P. D. Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, Shivakumar Vaithyanathan: SystemML: Declarative machine learning on MapReduce. ICDE 2011:231-242 31 Custom Algorithm Optimizer Resource Elasticity GPU Sampling Numeric Stability Task Parallelism 1st paper on Spark Compression
- 32.