layout: global title: Spark MLContext Programming Guide description: Spark MLContext Programming Guide

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Overview

The Spark MLContext API offers a programmatic interface for interacting with SystemML from Spark using languages such as Scala, Java, and Python. As a result, it offers a convenient way to interact with SystemML from the Spark Shell and from Notebooks such as Jupyter and Zeppelin.

NOTE: A new MLContext API has been redesigned for future SystemML releases. The old API is available in all versions of SystemML but will be deprecated and removed, so please migrate to the new API.

Spark Shell Example - NEW API

NOTE: The new MLContext API will be available in future SystemML releases. It can be used by building the project using Maven (‘mvn clean package’, or ‘mvn clean package -P distribution’). For SystemML version 0.10.0 and earlier, please see the documentation regarding the old API.

Start Spark Shell with SystemML

To use SystemML with Spark Shell, the SystemML jar can be referenced using Spark Shell's --jars option.

{% highlight bash %} spark-shell --executor-memory 4G --driver-memory 4G --jars SystemML.jar {% endhighlight %}

Create MLContext

All primary classes that a user interacts with are located in the org.apache.sysml.api.mlcontext package. For convenience, we can additionally add a static import of ScriptFactory to shorten the syntax for creating Script objects. An MLContext object can be created by passing its constructor a reference to the SparkContext. If successful, you should see a “Welcome to Apache SystemML!” message.

scala> import org.apache.sysml.api.mlcontext.ScriptFactory._ import org.apache.sysml.api.mlcontext.ScriptFactory._

scala> val ml = new MLContext(sc)

Welcome to Apache SystemML!

ml: org.apache.sysml.api.mlcontext.MLContext = org.apache.sysml.api.mlcontext.MLContext@12139db0

{% endhighlight %}

Hello World

The ScriptFactory class allows DML and PYDML scripts to be created from Strings, Files, URLs, and InputStreams. Here, we'll use the dml method to create a DML “hello world” script based on a String. Notice that the script reports that it has no inputs or outputs.

We execute the script using MLContext's execute method, which displays “hello world” to the console. The execute method returns an MLResults object, which contains no results since the script has no outputs.

Outputs: None

scala> ml.execute(helloScript) hello world res0: org.apache.sysml.api.mlcontext.MLResults = None

{% endhighlight %}

DataFrame Example

For demonstration purposes, we'll use Spark to create a DataFrame called df of random doubles from 0 to 1 consisting of 10,000 rows and 1,000 columns.

scala> import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import org.apache.spark.sql.types.{StructType, StructField, DoubleType}

scala> import scala.util.Random import scala.util.Random

scala> val numRows = 10000 numRows: Int = 10000

scala> val numCols = 1000 numCols: Int = 1000

scala> val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } data: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[1] at map at :42

scala> val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) schema: org.apache.spark.sql.types.StructType = StructType(StructField(C0,DoubleType,true), StructField(C1,DoubleType,true), StructField(C2,DoubleType,true), StructField(C3,DoubleType,true), StructField(C4,DoubleType,true), StructField(C5,DoubleType,true), StructField(C6,DoubleType,true), StructField(C7,DoubleType,true), StructField(C8,DoubleType,true), StructField(C9,DoubleType,true), StructField(C10,DoubleType,true), StructField(C11,DoubleType,true), StructField(C12,DoubleType,true), StructField(C13,DoubleType,true), StructField(C14,DoubleType,true), StructField(C15,DoubleType,true), StructField(C16,DoubleType,true), StructField(C17,DoubleType,true), StructField(C18,DoubleType,true), StructField(C19,DoubleType,true), StructField(C20,DoubleType,true), StructField(C21,DoubleType,true), ... scala> val df = sqlContext.createDataFrame(data, schema) df: org.apache.spark.sql.DataFrame = [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, C8: double, C9: double, C10: double, C11: double, C12: double, C13: double, C14: double, C15: double, C16: double, C17: double, C18: double, C19: double, C20: double, C21: double, C22: double, C23: double, C24: double, C25: double, C26: double, C27: double, C28: double, C29: double, C30: double, C31: double, C32: double, C33: double, C34: double, C35: double, C36: double, C37: double, C38: double, C39: double, C40: double, C41: double, C42: double, C43: double, C44: double, C45: double, C46: double, C47: double, C48: double, C49: double, C50: double, C51: double, C52: double, C53: double, C54: double, C55: double, C56: double, C57: double, C58: double, C5...

{% endhighlight %}

We'll create a DML script to find the minimum, maximum, and mean values in a matrix. This script has one input variable, matrix Xin, and three output variables, minOut, maxOut, and meanOut.

For performance, we'll specify metadata indicating that the matrix has 10,000 rows and 1,000 columns.

We'll create a DML script using the ScriptFactory dml method with the minMaxMean script String. The input variable is specified to be our DataFrame df with MatrixMetadata mm. The output variables are specified to be minOut, maxOut, and meanOut. Notice that inputs are supplied by the in method, and outputs are supplied by the out method.

We execute the script and obtain the results as a Tuple by calling getTuple on the results, specifying the types and names of the output variables.

{% endhighlight %}

scala> val mm = new MatrixMetadata(numRows, numCols) mm: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 10000, columns: 1000, non-zeros: None, rows per block: None, columns per block: None

scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...

Outputs: [1] minOut [2] maxOut [3] meanOut

scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”) min: Double = 2.6257349849956313E-8 max: Double = 0.9999999686609718 mean: Double = 0.49996223966662934

{% endhighlight %}

Many different types of input and output variables are automatically allowed. These types include Boolean, Long, Double, String, Array[Array[Double]], RDD<String> and JavaRDD<String> in CSV (dense) and IJV (sparse) formats, DataFrame, BinaryBlockMatrix, Matrix, and Frame. RDDs and JavaRDDs are assumed to be CSV format unless MatrixMetadata is supplied indicating IJV format.

RDD Example

Let‘s take a look at an example of input matrices as RDDs in CSV format. We’ll create two 2x2 matrices and input these into a DML script. This script will sum each matrix and create a message based on which sum is greater. We will output the sums and the message.

For fun, we‘ll write the script String to a file and then use ScriptFactory’s dmlFromFile method to create the script object based on the file. We'll also specify the inputs using a Map, although we could have also chained together two in methods to specify the same inputs.

scala> val rdd2 = sc.parallelize(Array(“5.0,6.0”, “7.0,8.0”)) rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[43] at parallelize at :38

scala> val sums = """ | s1 = sum(m1); | s2 = sum(m2); | if (s1 > s2) { | message = “s1 is greater” | } else if (s2 > s1) { | message = “s2 is greater” | } else { | message = “s1 and s2 are equal” | } | """ sums: String = " s1 = sum(m1); s2 = sum(m2); if (s1 > s2) { message = “s1 is greater” } else if (s2 > s1) { message = “s2 is greater” } else { message = “s1 and s2 are equal” } "

scala> scala.tools.nsc.io.File(“sums.dml”).writeAll(sums)

scala> val sumScript = dmlFromFile(“sums.dml”).in(Map(“m1”-> rdd1, “m2”-> rdd2)).out(“s1”, “s2”, “message”) sumScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m1: ParallelCollectionRDD[42] at parallelize at :38 [2] (RDD) m2: ParallelCollectionRDD[43] at parallelize at :38

Outputs: [1] s1 [2] s2 [3] message

scala> val sumResults = ml.execute(sumScript) sumResults: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) s1: 10.0 [2] (Double) s2: 26.0 [3] (String) message: s2 is greater

scala> val s1 = sumResults.getDouble(“s1”) s1: Double = 10.0

scala> val s2 = sumResults.getDouble(“s2”) s2: Double = 26.0

scala> val message = sumResults.getString(“message”) message: String = s2 is greater

{% endhighlight %}

If you have metadata that you would like to supply along with the input matrices, this can be accomplished using a Scala Seq, List, or Array.

{% endhighlight %}

scala> val rdd2Metadata = new MatrixMetadata(2, 2) rdd2Metadata: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 2, columns: 2, non-zeros: None, rows per block: None, columns per block: None

scala> val sumScript = dmlFromFile(“sums.dml”).in(Seq((“m1”, rdd1, rdd1Metadata), (“m2”, rdd2, rdd2Metadata))).out(“s1”, “s2”, “message”) sumScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m1: ParallelCollectionRDD[42] at parallelize at :38 [2] (RDD) m2: ParallelCollectionRDD[43] at parallelize at :38

Outputs: [1] s1 [2] s2 [3] message

scala> val (firstSum, secondSum, sumMessage) = ml.execute(sumScript).getTuple[Double, Double, String](“s1”, “s2”, “message”) firstSum: Double = 10.0 secondSum: Double = 26.0 sumMessage: String = s2 is greater

{% endhighlight %}

The same inputs with metadata can be supplied by chaining in methods, as in the example below, which shows that out methods can also be chained.

{% endhighlight %}

Outputs: [1] s1 [2] s2 [3] message

scala> val (firstSum, secondSum, sumMessage) = ml.execute(sumScript).getTuple[Double, Double, String](“s1”, “s2”, “message”) firstSum: Double = 10.0 secondSum: Double = 26.0 sumMessage: String = s2 is greater

{% endhighlight %}

Matrix Output

Let‘s look at an example of reading a matrix out of SystemML. We’ll create a DML script in which we create a 2x2 matrix m. We'll set the variable n to be the sum of the cells in the matrix.

We create a script object using String s, and we set m and n as the outputs. We execute the script, and in the results we see we have Matrix m and Double n. The n output variable has a value of 110.0.

We get Matrix m and Double n as a Tuple of values x and y. We then convert Matrix m to an RDD of IJV values, an RDD of CSV values, a DataFrame, and a two-dimensional Double Array, and we display the values in each of these data structures.

{% endhighlight %}

scala> val scr = dml(s).out(“m”, “n”); scr: org.apache.sysml.api.mlcontext.Script = Inputs: None

Outputs: [1] m [2] n

scala> val res = ml.execute(scr) res: org.apache.sysml.api.mlcontext.MLResults = [1] (Matrix) m: Matrix: scratch_space//_p12059_9.31.117.12//_t0/temp26_14, [2 x 2, nnz=4, blocks (1000 x 1000)], binaryblock, dirty [2] (Double) n: 110.0

scala> val (x, y) = res.getTupleMatrix, Double x: org.apache.sysml.api.mlcontext.Matrix = Matrix: scratch_space//_p12059_9.31.117.12//_t0/temp26_14, [2 x 2, nnz=4, blocks (1000 x 1000)], binaryblock, dirty y: Double = 110.0

scala> x.toRDDStringIJV.collect.foreach(println) 1 1 11.0 1 2 22.0 2 1 33.0 2 2 44.0

scala> x.toRDDStringCSV.collect.foreach(println) 11.0,22.0 33.0,44.0

scala> x.toDF.collect.foreach(println) [0.0,11.0,22.0] [1.0,33.0,44.0]

scala> x.to2DDoubleArray res10: Array[Array[Double]] = Array(Array(11.0, 22.0), Array(33.0, 44.0))

{% endhighlight %}

Univariate Statistics on Haberman Data

Our next example will involve Haberman's Survival Data Set in CSV format from the Center for Machine Learning and Intelligent Systems. We will run the SystemML Univariate Statistics (“Univar-Stats.dml”) script on this data.

We‘ll pull the data from a URL and convert it to an RDD, habermanRDD. Next, we’ll create metadata, habermanMetadata, stating that the matrix consists of 306 rows and 4 columns.

As we can see from the comments in the script here, the script requires a ‘TYPES’ input matrix that lists the types of the features (1 for scale, 2 for nominal, 3 for ordinal), so we create a typesRDD matrix consisting of 1 row and 4 columns, with corresponding metadata, typesMetadata.

Next, we create the DML script object called uni using ScriptFactory‘s dmlFromUrl method, specifying the GitHub URL where the DML script is located. We bind the habermanRDD matrix to the A variable in Univar-Stats.dml, and we bind the typesRDD matrix to the K variable. In addition, we supply a $CONSOLE_OUTPUT parameter with a Boolean value of true, which indicates that we’d like to output labeled results to the console. We'll explain why we bind to the A and K variables in the Input Variables vs Input Parameters section below.

{% endhighlight %}

scala> val habermanList = scala.io.Source.fromURL(habermanUrl).mkString.split(“\n”) habermanList: Array[String] = Array(30,64,1,1, 30,62,3,1, 30,65,0,1, 31,59,2,1, 31,65,4,1, 33,58,10,1, 33,60,0,1, 34,59,0,2, 34,66,9,2, 34,58,30,1, 34,60,1,1, 34,61,10,1, 34,67,7,1, 34,60,0,1, 35,64,13,1, 35,63,0,1, 36,60,1,1, 36,69,0,1, 37,60,0,1, 37,63,0,1, 37,58,0,1, 37,59,6,1, 37,60,15,1, 37,63,0,1, 38,69,21,2, 38,59,2,1, 38,60,0,1, 38,60,0,1, 38,62,3,1, 38,64,1,1, 38,66,0,1, 38,66,11,1, 38,60,1,1, 38,67,5,1, 39,66,0,2, 39,63,0,1, 39,67,0,1, 39,58,0,1, 39,59,2,1, 39,63,4,1, 40,58,2,1, 40,58,0,1, 40,65,0,1, 41,60,23,2, 41,64,0,2, 41,67,0,2, 41,58,0,1, 41,59,8,1, 41,59,0,1, 41,64,0,1, 41,69,8,1, 41,65,0,1, 41,65,0,1, 42,69,1,2, 42,59,0,2, 42,58,0,1, 42,60,1,1, 42,59,2,1, 42,61,4,1, 42,62,20,1, 42,65,0,1, 42,63,1,1, 43,58,52,2, 43,59,2,2, 43,64,0,2, 43,64,0,2, 43,63,14,1, 43,64,2,1, 43... scala> val habermanRDD = sc.parallelize(habermanList) habermanRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[159] at parallelize at :43

scala> val habermanMetadata = new MatrixMetadata(306, 4) habermanMetadata: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 306, columns: 4, non-zeros: None, rows per block: None, columns per block: None

scala> val typesRDD = sc.parallelize(Array(“1.0,1.0,1.0,2.0”)) typesRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[160] at parallelize at :39

scala> val typesMetadata = new MatrixMetadata(1, 4) typesMetadata: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 1, columns: 4, non-zeros: None, rows per block: None, columns per block: None

scala> val scriptUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” scriptUrl: String = https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml

scala> val uni = dmlFromUrl(scriptUrl).in(“A”, habermanRDD, habermanMetadata).in(“K”, typesRDD, typesMetadata).in(“$CONSOLE_OUTPUT”, true) uni: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) A: ParallelCollectionRDD[159] at parallelize at :43 [2] (RDD) K: ParallelCollectionRDD[160] at parallelize at :39 [3] (Boolean) $CONSOLE_OUTPUT: true

Outputs: None

scala> ml.execute(uni) ...

Feature [1]: Scale (01) Minimum | 30.0 (02) Maximum | 83.0 (03) Range | 53.0 (04) Mean | 52.45751633986928 (05) Variance | 116.71458266366658 (06) Std deviation | 10.803452349303281 (07) Std err of mean | 0.6175922641866753 (08) Coeff of variation | 0.20594669940735139 (09) Skewness | 0.1450718616532357 (10) Kurtosis | -0.6150152487211726 (11) Std err of skewness | 0.13934809593495995 (12) Std err of kurtosis | 0.277810485320835 (13) Median | 52.0 (14) Interquartile mean | 52.16013071895425

Feature [2]: Scale (01) Minimum | 58.0 (02) Maximum | 69.0 (03) Range | 11.0 (04) Mean | 62.85294117647059 (05) Variance | 10.558630665380907 (06) Std deviation | 3.2494046632238507 (07) Std err of mean | 0.18575610076612029 (08) Coeff of variation | 0.051698529971741194 (09) Skewness | 0.07798443581479181 (10) Kurtosis | -1.1324380182967442 (11) Std err of skewness | 0.13934809593495995 (12) Std err of kurtosis | 0.277810485320835 (13) Median | 63.0 (14) Interquartile mean | 62.80392156862745

Feature [3]: Scale (01) Minimum | 0.0 (02) Maximum | 52.0 (03) Range | 52.0 (04) Mean | 4.026143790849673 (05) Variance | 51.691117539912135 (06) Std deviation | 7.189653506248555 (07) Std err of mean | 0.41100513466216837 (08) Coeff of variation | 1.7857418611299172 (09) Skewness | 2.954633471088322 (10) Kurtosis | 11.425776549251449 (11) Std err of skewness | 0.13934809593495995 (12) Std err of kurtosis | 0.277810485320835 (13) Median | 1.0 (14) Interquartile mean | 1.2483660130718954

Feature [4]: Categorical (Nominal) (15) Num of categories | 2 (16) Mode | 1 (17) Num of modes | 1 res23: org.apache.sysml.api.mlcontext.MLResults = None

{% endhighlight %}

Alternatively, we could supply a java.net.URL to the Script in method. Note that if the URL matrix data is in IJV format, metadata needs to be supplied for the matrix.

scala> val typesRDD = sc.parallelize(Array(“1.0,1.0,1.0,2.0”)) typesRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[50] at parallelize at :33

scala> val scriptUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” scriptUrl: String = https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml

scala> val uni = dmlFromUrl(scriptUrl).in(“A”, new java.net.URL(habermanUrl)).in(“K”, typesRDD).in(“$CONSOLE_OUTPUT”, true) uni: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (URL) A: http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data [2] (RDD) K: ParallelCollectionRDD[50] at parallelize at :33 [3] (Boolean) $CONSOLE_OUTPUT: true

Outputs: None

scala> ml.execute(uni) ...

(01) Minimum | 30.0 (02) Maximum | 83.0 (03) Range | 53.0 (04) Mean | 52.45751633986928 (05) Variance | 116.71458266366658 (06) Std deviation | 10.803452349303281 (07) Std err of mean | 0.6175922641866753 (08) Coeff of variation | 0.20594669940735139 (09) Skewness | 0.1450718616532357 (10) Kurtosis | -0.6150152487211726 (11) Std err of skewness | 0.13934809593495995 (12) Std err of kurtosis | 0.277810485320835 (13) Median | 52.0 (14) Interquartile mean | 52.16013071895425 Feature [1]: Scale

(01) Minimum | 58.0 (02) Maximum | 69.0 (03) Range | 11.0 (04) Mean | 62.85294117647059 (05) Variance | 10.558630665380907 (06) Std deviation | 3.2494046632238507 (07) Std err of mean | 0.18575610076612029 (08) Coeff of variation | 0.051698529971741194 (09) Skewness | 0.07798443581479181 (10) Kurtosis | -1.1324380182967442 (11) Std err of skewness | 0.13934809593495995 (12) Std err of kurtosis | 0.277810485320835 (13) Median | 63.0 (14) Interquartile mean | 62.80392156862745 Feature [2]: Scale

(01) Minimum | 0.0 (02) Maximum | 52.0 (03) Range | 52.0 (04) Mean | 4.026143790849673 (05) Variance | 51.691117539912135 (06) Std deviation | 7.189653506248555 (07) Std err of mean | 0.41100513466216837 (08) Coeff of variation | 1.7857418611299172 (09) Skewness | 2.954633471088322 (10) Kurtosis | 11.425776549251449 (11) Std err of skewness | 0.13934809593495995 (12) Std err of kurtosis | 0.277810485320835 (13) Median | 1.0 (14) Interquartile mean | 1.2483660130718954 Feature [3]: Scale

Feature [4]: Categorical (Nominal) (15) Num of categories | 2 (16) Mode | 1 (17) Num of modes | 1 res5: org.apache.sysml.api.mlcontext.MLResults = None

{% endhighlight %}

Input Variables vs Input Parameters

If we examine the Univar-Stats.dml file, we see in the comments that it can take 4 input parameters, $X, $TYPES, $CONSOLE_OUTPUT, and $STATS. Input parameters are typically useful when executing SystemML in Standalone mode, Spark batch mode, or Hadoop batch mode. For example, $X specifies the location in the file system where the input data matrix is located, $TYPES specifies the location in the file system where the input types matrix is located, $CONSOLE_OUTPUT specifies whether or not labeled statistics should be output to the console, and $STATS specifies the location in the file system where the output matrix should be written.

{% highlight r %} ...

INPUT PARAMETERS:

-------------------------------------------------------------------------------------------------

NAME TYPE DEFAULT MEANING

-------------------------------------------------------------------------------------------------

X String --- Location of INPUT data matrix

TYPES String --- Location of INPUT matrix that lists the types of the features:

1 for scale, 2 for nominal, 3 for ordinal

CONSOLE_OUTPUT Boolean FALSE If TRUE, print summary statistics to console

STATS String --- Location of OUTPUT matrix with summary statistics computed for

all features (17 statistics - 14 scale, 3 categorical)

-------------------------------------------------------------------------------------------------

OUTPUT: Matrix of summary statistics

... consoleOutput = ifdef($CONSOLE_OUTPUT, FALSE); A = read($X); # data file K = read($TYPES); # attribute kind file ... write(baseStats, $STATS); ... {% endhighlight %}

Because MLContext is a programmatic interface, it offers more flexibility. You can still use input parameters and files in the file system, such as this example that specifies file paths to the input matrices and the output matrix:

{% highlight scala %} val script = dmlFromFile(“scripts/algorithms/Univar-Stats.dml”).in(“$X”, “data/haberman.data”).in(“$TYPES”, “data/types.csv”).in(“$STATS”, “data/univarOut.mtx”).in(“$CONSOLE_OUTPUT”, true) ml.execute(script) {% endhighlight %}

Using the MLContext API, rather than relying solely on input parameters, we can bind to the variables associated with the read and write statements. In the fragment of Univar-Stats.dml above, notice that the matrix at path $X is read to variable A, $TYPES is read to variable K, and baseStats is written to path $STATS. Therefore, we can bind the Haberman input data matrix to the A variable, the input types matrix to the K variable, and the output matrix to the baseStats variable.

Outputs: [1] baseStats

scala> val baseStats = ml.execute(uni).getMatrix(“baseStats”) ... baseStats: org.apache.sysml.api.mlcontext.Matrix = Matrix: scratch_space/_p12059_9.31.117.12/parfor/4_resultmerge1, [17 x 4, nnz=44, blocks (1000 x 1000)], binaryblock, dirty

scala> baseStats.toRDDStringIJV.collect.slice(0,9).foreach(println) 1 1 30.0 1 2 58.0 1 3 0.0 1 4 0.0 2 1 83.0 2 2 69.0 2 3 52.0 2 4 0.0 3 1 53.0

{% endhighlight %}

Script Information

The info method on a Script object can provide useful information about a DML or PyDML script, such as the inputs, output, symbol table, script string, and the script execution string that is passed to the internals of SystemML.

scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...

Outputs: [1] minOut [2] maxOut [3] meanOut

scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”) min: Double = 1.4149740823476975E-7 max: Double = 0.9999999956646207 mean: Double = 0.5000954668004209

scala> println(minMaxMeanScript.info) Script Type: DML

Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...

Outputs: [1] (Double) minOut: 1.4149740823476975E-7 [2] (Double) maxOut: 0.9999999956646207 [3] (Double) meanOut: 0.5000954668004209

Input Parameters: None

Input Variables: [1] Xin

Output Variables: [1] minOut [2] maxOut [3] meanOut

Symbol Table: [1] (Double) meanOut: 0.5000954668004209 [2] (Double) maxOut: 0.9999999956646207 [3] (Double) minOut: 1.4149740823476975E-7 [4] (Matrix) Xin: Matrix: scratch_space/temp_1166464711339222, [10000 x 1000, nnz=10000000, blocks (1000 x 1000)], binaryblock, not-dirty

Script String:

minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin)

Script Execution String: Xin = read('');

minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin) write(minOut, ''); write(maxOut, ''); write(meanOut, '');

{% endhighlight %}

Clearing Scripts and MLContext

Dealing with large matrices can require a significant amount of memory. To deal help deal with this, you can call a Script object's clearAll method to clear the inputs, outputs, symbol table, and script string. In terms of memory, the symbol table is most important because it holds references to matrices.

In this example, we display the symbol table of the minMaxMeanScript, call clearAll on the script, and then display the symbol table, which is empty.

{% endhighlight %}

scala> minMaxMeanScript.clearAll

scala> println(minMaxMeanScript.displaySymbolTable) Symbol Table: None

{% endhighlight %}

The MLContext object holds references to the scripts that have been executed. Calling clear on the MLContext clears all scripts that it has references to and then removes the references to these scripts.

{% highlight scala %} ml.clear {% endhighlight %}

Statistics

Statistics about script executions can be output to the console by calling MLContext's setStatistics method with a value of true.

{% endhighlight %}

scala> val minMaxMean = | """ | minOut = min(Xin) | maxOut = max(Xin) | meanOut = mean(Xin) | """ minMaxMean: String = " minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin) "

scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...

Outputs: [1] minOut [2] maxOut [3] meanOut

scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”) SystemML Statistics: Total elapsed time: 0.000 sec. Total compilation time: 0.000 sec. Total execution time: 0.000 sec. Number of compiled Spark inst: 0. Number of executed Spark inst: 0. Cache hits (Mem, WB, FS, HDFS): 2/0/0/1. Cache writes (WB, FS, HDFS): 1/0/0. Cache times (ACQr/m, RLS, EXP): 3.137/0.000/0.001/0.000 sec. HOP DAGs recompiled (PRED, SB): 0/0. HOP DAGs recompile time: 0.000 sec. Spark ctx create time (lazy): 0.000 sec. Spark trans counts (par,bc,col):0/0/2. Spark trans times (par,bc,col): 0.000/0.000/6.434 secs. Total JIT compile time: 112.372 sec. Total JVM GC count: 54. Total JVM GC time: 9.664 sec. Heavy hitter instructions (name, time, count): -- 1) uamin 3.150 sec 1 -- 2) uamean 0.021 sec 1 -- 3) uamax 0.017 sec 1 -- 4) rmvar 0.000 sec 3 -- 5) assignvar 0.000 sec 3

min: Double = 2.4982850344024143E-8 max: Double = 0.9999997007231808 mean: Double = 0.5002109404821844

{% endhighlight %}

Explain

A DML or PyDML script is converted into a SystemML program during script execution. Information about this program can be displayed by calling MLContext's setExplain method with a value of true.

{% endhighlight %}

scala> val minMaxMean = | """ | minOut = min(Xin) | maxOut = max(Xin) | meanOut = mean(Xin) | """ minMaxMean: String = " minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin) "

scala> val mm = new MatrixMetadata(numRows, numCols) mm: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 10000, columns: 1000, non-zeros: None, rows per block: None, columns per block: None

scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...

Outputs: [1] minOut [2] maxOut [3] meanOut

scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”)

PROGRAM --MAIN PROGRAM ----GENERIC (lines 1-8) [recompile=false] ------(12) TRead Xin [10000,1000,1000,1000,10000000] [0,0,76 -> 76MB] [chkpt], CP ------(13) ua(minRC) (12) [0,0,-1,-1,-1] [76,0,0 -> 76MB], CP ------(21) TWrite minOut (13) [0,0,-1,-1,-1] [0,0,0 -> 0MB], CP ------(14) ua(maxRC) (12) [0,0,-1,-1,-1] [76,0,0 -> 76MB], CP ------(27) TWrite maxOut (14) [0,0,-1,-1,-1] [0,0,0 -> 0MB], CP ------(15) ua(meanRC) (12) [0,0,-1,-1,-1] [76,0,0 -> 76MB], CP ------(33) TWrite meanOut (15) [0,0,-1,-1,-1] [0,0,0 -> 0MB], CP

min: Double = 5.16651366133658E-9 max: Double = 0.9999999368927975 mean: Double = 0.5001096515241128

{% endhighlight %}

Different explain levels can be set. The explain levels are NONE, HOPS, RUNTIME, RECOMPILE_HOPS, and RECOMPILE_RUNTIME.

scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”)

PROGRAM ( size CP/SP = 9/0 ) --MAIN PROGRAM ----GENERIC (lines 1-8) [recompile=false] ------CP uamin Xin.MATRIX.DOUBLE _Var8.SCALAR.DOUBLE 8 ------CP uamax Xin.MATRIX.DOUBLE _Var9.SCALAR.DOUBLE 8 ------CP uamean Xin.MATRIX.DOUBLE _Var10.SCALAR.DOUBLE 8 ------CP assignvar _Var8.SCALAR.DOUBLE.false minOut.SCALAR.DOUBLE ------CP assignvar _Var9.SCALAR.DOUBLE.false maxOut.SCALAR.DOUBLE ------CP assignvar _Var10.SCALAR.DOUBLE.false meanOut.SCALAR.DOUBLE ------CP rmvar _Var8 ------CP rmvar _Var9 ------CP rmvar _Var10

min: Double = 5.16651366133658E-9 max: Double = 0.9999999368927975 mean: Double = 0.5001096515241128

{% endhighlight %}

Script Creation and ScriptFactory

Script objects can be created using standard Script constructors. A Script can be of two types: DML (R-based syntax) and PYDML (Python-based syntax). If no ScriptType is specified, the default Script type is DML.

scala> val script = new Script(ScriptType.PYDML); ... scala> println(script.getScriptType) PYDML

{% endhighlight %}

The ScriptFactory class offers convenient methods for creating DML and PYDML scripts from a variety of sources. ScriptFactory can create a script object from a String, File, URL, or InputStream.

Script from URL:

Here we create Script object s1 by reading Univar-Stats.dml from a URL.

{% highlight scala %} val uniUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” val s1 = ScriptFactory.dmlFromUrl(scriptUrl) {% endhighlight %}

Script from String:

We create Script objects s2 and s3 from Strings using ScriptFactory's dml and dmlFromString methods. Both methods perform the same action. This example reads an algorithm at a URL to String uniString and then creates two script objects based on this String.

{% highlight scala %} val uniUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” val uniString = scala.io.Source.fromURL(uniUrl).mkString val s2 = ScriptFactory.dml(uniString) val s3 = ScriptFactory.dmlFromString(uniString) {% endhighlight %}

Script from File:

We create Script object s4 based on a path to a file using ScriptFactory's dmlFromFile method. This example reads a URL to a String, writes this String to a file, and then uses the path to the file to create a Script object.

{% highlight scala %} val uniUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” val uniString = scala.io.Source.fromURL(uniUrl).mkString scala.tools.nsc.io.File(“uni.dml”).writeAll(uniString) val s4 = ScriptFactory.dmlFromFile(“uni.dml”) {% endhighlight %}

Script from InputStream:

The SystemML jar file contains all the primary algorithm scripts. We can read one of these scripts as an InputStream and use this to create a Script object.

{% highlight scala %} val inputStream = getClass.getResourceAsStream(“/scripts/algorithms/Univar-Stats.dml”) val s5 = ScriptFactory.dmlFromInputStream(inputStream) {% endhighlight %}

Script from Resource:

As mentioned, the SystemML jar file contains all the primary algorithm script files. For convenience, we can read these script files or other script files on the classpath using ScriptFactory's dmlFromResource and pydmlFromResource methods.

{% highlight scala %} val s6 = ScriptFactory.dmlFromResource(“/scripts/algorithms/Univar-Stats.dml”); {% endhighlight %}

ScriptExecutor

A Script is executed by a ScriptExecutor. If no ScriptExecutor is specified, a default ScriptExecutor will be created to execute a Script. Script execution consists of several steps, as detailed in SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. Additional information can be found in the Javadocs for ScriptExecutor.

Advanced users may find it useful to be able to specify their own execution or to override ScriptExecutor methods by subclassing ScriptExecutor.

In this example, we override the parseScript and validateScript methods to display messages to the console during these execution steps.

{% endhighlight %}

scala> val helloScript = dml(“print(‘hello world’)”) helloScript: org.apache.sysml.api.mlcontext.Script = Inputs: None

Outputs: None

scala> ml.execute(helloScript, new MyScriptExecutor) Parsing script Validating script hello world res63: org.apache.sysml.api.mlcontext.MLResults = None

{% endhighlight %}

MatrixMetadata

When supplying matrix data to Apache SystemML using the MLContext API, matrix metadata can be supplied using a MatrixMetadata object. Supplying characteristics about a matrix can significantly improve performance. For some types of input matrices, supplying metadata is mandatory. Metadata at a minimum typically consists of the number of rows and columns in a matrix. The number of non-zeros can also be supplied.

Additionally, the number of rows and columns per block can be supplied, although in typical usage it's probably fine to use the default values used by SystemML (1,000 rows and 1,000 columns per block). SystemML handles a matrix internally by splitting the matrix into chunks, or blocks. The number of rows and columns per block refers to the size of these matrix blocks.

CSV RDD with No Metadata:

Here we see an example of inputting an RDD of Strings in CSV format with no metadata. Note that in general it is recommended that metadata is supplied. We output the sum and mean of the cells in the matrix.

{% endhighlight %}

scala> val sumAndMean = dml(“sum = sum(m); mean = mean(m)”).in(“m”, rddCSV).out(“sum”, “mean”) sumAndMean: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m: ParallelCollectionRDD[190] at parallelize at :38

Outputs: [1] sum [2] mean

scala> ml.execute(sumAndMean) res20: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) sum: 10.0 [2] (Double) mean: 2.5

{% endhighlight %}

IJV RDD with Metadata:

Next, we‘ll supply an RDD in IJV format. IJV is a sparse format where each line has three space-separated values. The first value indicates the row number, the second value indicates the column number, and the third value indicates the cell value. Since the total numbers of rows and columns can’t be determined from these IJV rows, we need to supply metadata describing the matrix size.

Here, we specify that our matrix has 3 rows and 3 columns.

{% endhighlight %}

scala> val mm3x3 = new MatrixMetadata(MatrixFormat.IJV, 3, 3) mm3x3: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 3, columns: 3, non-zeros: None, rows per block: None, columns per block: None

scala> val sumAndMean = dml(“sum = sum(m); mean = mean(m)”).in(“m”, rddIJV, mm3x3).out(“sum”, “mean”) sumAndMean: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m: ParallelCollectionRDD[202] at parallelize at :38

Outputs: [1] sum [2] mean

scala> ml.execute(sumAndMean) res21: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) sum: 10.0 [2] (Double) mean: 1.1111111111111112

{% endhighlight %}

Next, we‘ll run the same DML, but this time we’ll specify that the input matrix is 4x4 instead of 3x3.

{% endhighlight %}

scala> val mm4x4 = new MatrixMetadata(MatrixFormat.IJV, 4, 4) mm4x4: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 4, columns: 4, non-zeros: None, rows per block: None, columns per block: None

scala> val sumAndMean = dml(“sum = sum(m); mean = mean(m)”).in(“m”, rddIJV, mm4x4).out(“sum”, “mean”) sumAndMean: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m: ParallelCollectionRDD[210] at parallelize at :38

Outputs: [1] sum [2] mean

scala> ml.execute(sumAndMean) res22: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) sum: 10.0 [2] (Double) mean: 0.625

{% endhighlight %}

Matrix Data Conversions and Performance

Internally, Apache SystemML uses a binary-block matrix representation, where a matrix is represented as a grouping of blocks. Each block is equal in size to the other blocks in the matrix and consists of a number of rows and columns. The default block size is 1,000 rows by 1,000 columns.

Conversion of a large set of data to a SystemML matrix representation can potentially be time-consuming. Therefore, if you use a set of data multiple times, one way to potentially improve performance is to convert it to a SystemML matrix representation and then use this representation rather than performing the data conversion each time.

There are currently two mechanisms for this in SystemML: (1) BinaryBlockMatrix and (2) Matrix.

BinaryBlockMatrix:

If you have an input DataFrame, it can be converted to a BinaryBlockMatrix, and this BinaryBlockMatrix can be passed as an input rather than passing in the DataFrame as an input.

For example, suppose we had a 10000x1000 matrix represented as a DataFrame, as we saw in an earlier example. Now suppose we create two Script objects with the DataFrame as an input, as shown below. In the Spark Shell, when executing this code, you can see that each of the two Script object creations requires the time-consuming data conversion step.

{% highlight scala %} import org.apache.spark.sql._ import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import scala.util.Random val numRows = 10000 val numCols = 1000 val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) val df = sqlContext.createDataFrame(data, schema) val mm = new MatrixMetadata(numRows, numCols) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) {% endhighlight %}

Rather than passing in a DataFrame each time to the Script object creation, let's instead create a BinaryBlockMatrix object based on the DataFrame and pass this BinaryBlockMatrix to the Script object creation. If we run the code below in the Spark Shell, we see that the data conversion step occurs when the BinaryBlockMatrix object is created. However, when we create a Script object twice, we see that no conversion penalty occurs, since this conversion occurred when the BinaryBlockMatrix was created.

{% highlight scala %} import org.apache.spark.sql._ import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import scala.util.Random val numRows = 10000 val numCols = 1000 val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) val df = sqlContext.createDataFrame(data, schema) val mm = new MatrixMetadata(numRows, numCols) val bbm = new BinaryBlockMatrix(df, mm) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, bbm).out(“minOut”, “maxOut”, “meanOut”) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, bbm).out(“minOut”, “maxOut”, “meanOut”) {% endhighlight %}

Matrix:

When a matrix is returned as an output, it is returned as a Matrix object, which is a wrapper around a SystemML MatrixObject. As a result, an output Matrix is already in a SystemML representation, meaning that it can be passed as an input with no data conversion penalty.

As an example, here we read in matrix x as an RDD in CSV format. We create a Script that adds one to all values in the matrix. We obtain the resulting matrix y as a Matrix. We execute the script five times, feeding the output matrix as the input matrix for the next script execution.

{% endhighlight %}

scala> val add = dml(“y = x + 1”).in(“x”, rddCSV).out(“y”) add: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) x: ParallelCollectionRDD[341] at parallelize at :53

Outputs: [1] y

scala> for (i <- 1 to 5) { | println(“#” + i + “:”); | val m = ml.execute(add).getMatrix(“y”) | m.toRDDStringCSV.collect.foreach(println) | add.in(“x”, m) | } #1: 2.0,3.0 4.0,5.0 #2: 3.0,4.0 5.0,6.0 #3: 4.0,5.0 6.0,7.0 #4: 5.0,6.0 7.0,8.0 #5: 6.0,7.0 8.0,9.0

{% endhighlight %}


Jupyter (PySpark) Notebook Example - Poisson Nonnegative Matrix Factorization

Similar to the Scala API, SystemML also provides a Python MLContext API. In addition to the regular SystemML.jar file, you'll need to install the Python API as follows:

  • Latest release:
    • Python 2:

      pip install systemml
      # Bleeding edge: pip install git+git://github.com/apache/incubator-systemml.git#subdirectory=src/main/python
      
    • Python 3:

      pip3 install systemml
      # Bleeding edge: pip3 install git+git://github.com/apache/incubator-systemml.git#subdirectory=src/main/python
      
  • Don't forget to download the SystemML.jar file, which can be found in the latest release, or in a nightly build.

Here, we'll explore the use of SystemML via PySpark in a Jupyter notebook. This Jupyter notebook example can be nicely viewed in a rendered state on GitHub, and can be downloaded here to a directory of your choice.

From the directory with the downloaded notebook, start Jupyter with PySpark:

  • Python 2:

    PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --master local[*] --driver-class-path SystemML.jar --jars SystemML.jar
    
  • Python 3:

    PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --master local[*] --driver-class-path SystemML.jar --jars SystemML.jar
    

This will open Jupyter in a browser:

Jupyter Notebook

We can then open up the SystemML-PySpark-Recommendation-Demo notebook.

Set up the notebook and download the data

{% highlight python %} %load_ext autoreload %autoreload 2 %matplotlib inline

import numpy as np import matplotlib.pyplot as plt from systemml import MLContext, dml # pip install systeml plt.rcParams[‘figure.figsize’] = (10, 6) {% endhighlight %}

{% highlight python %} %%sh

Download dataset

curl -O http://snap.stanford.edu/data/amazon0601.txt.gz gunzip amazon0601.txt.gz {% endhighlight %}

Use PySpark to load the data in as a Spark DataFrame

{% highlight python %}

Load data

import pyspark.sql.functions as F dataPath = “amazon0601.txt”

X_train = (sc.textFile(dataPath) .filter(lambda l: not l.startswith(“#”)) .map(lambda l: l.split(“\t”)) .map(lambda prods: (int(prods[0]), int(prods[1]), 1.0)) .toDF((“prod_i”, “prod_j”, “x_ij”)) .filter(“prod_i < 500 AND prod_j < 500”) # Filter for memory constraints .cache())

max_prod_i = X_train.select(F.max(“prod_i”)).first()[0] max_prod_j = X_train.select(F.max(“prod_j”)).first()[0] numProducts = max(max_prod_i, max_prod_j) + 1 # 0-based indexing print(“Total number of products: {}”.format(numProducts)) {% endhighlight %}

Create a SystemML MLContext object

{% highlight python %}

Create SystemML MLContext

ml = MLContext(sc) {% endhighlight %}

Define a kernel for Poisson nonnegative matrix factorization (PNMF) in DML

{% highlight python %}

Define PNMF kernel in SystemML's DSL using the R-like syntax for PNMF

pnmf = """

data & args

X = X+1 # change product IDs to be 1-based, rather than 0-based V = table(X[,1], X[,2]) size = ifdef($size, -1) if(size > -1) { V = V[1:size,1:size] }

n = nrow(V) m = ncol(V) range = 0.01 W = Rand(rows=n, cols=rank, min=0, max=range, pdf=“uniform”) H = Rand(rows=rank, cols=m, min=0, max=range, pdf=“uniform”) losses = matrix(0, rows=max_iter, cols=1)

run PNMF

i=1 while(i <= max_iter) {

update params

H = (H * (t(W) %% (V/(W%%H))))/t(colSums(W)) W = (W * ((V/(W%%H)) %% t(H)))/t(rowSums(H))

compute loss

losses[i,] = -1 * (sum(Vlog(W%%H)) - as.scalar(colSums(W)%*%rowSums(H))) i = i + 1; } """ {% endhighlight %}

Execute the algorithm

{% highlight python %}

Run the PNMF script on SystemML with Spark

script = dml(pnmf).input(X=X_train, max_iter=100, rank=10).output(“W”, “H”, “losses”) W, H, losses = ml.execute(script).get(“W”, “H”, “losses”) {% endhighlight %}

Retrieve the losses during training and plot them

{% highlight python %}

Plot training loss over time

xy = losses.toDF().sort(“__INDEX”).map(lambda r: (r[0], r[1])).collect() x, y = zip(*xy) plt.plot(x, y) plt.xlabel(‘Iteration’) plt.ylabel(‘Loss’) plt.title(‘PNMF Training Loss’) {% endhighlight %}

Jupyter Loss Graph


Spark Shell Example - OLD API

Start Spark Shell with SystemML

To use SystemML with the Spark Shell, the SystemML jar can be referenced using the Spark Shell's --jars option. Instructions to build the SystemML jar can be found in the SystemML GitHub README.

{% highlight bash %} ./bin/spark-shell --executor-memory 4G --driver-memory 4G --jars SystemML.jar {% endhighlight %}

Here is an example of Spark Shell with SystemML and YARN.

{% highlight bash %} ./bin/spark-shell --master yarn-client --num-executors 3 --driver-memory 5G --executor-memory 5G --executor-cores 4 --jars SystemML.jar {% endhighlight %}

Create MLContext

An MLContext object can be created by passing its constructor a reference to the SparkContext.

scala> val ml = new MLContext(sc) ml: org.apache.sysml.api.MLContext = org.apache.sysml.api.MLContext@33e38c6b {% endhighlight %}

Create DataFrame

For demonstration purposes, we'll create a DataFrame consisting of 100,000 rows and 1,000 columns of random doubles.

scala> import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import org.apache.spark.sql.types.{StructType, StructField, DoubleType}

scala> import scala.util.Random import scala.util.Random

scala> val numRows = 100000 numRows: Int = 100000

scala> val numCols = 1000 numCols: Int = 1000

scala> val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } data: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[1] at map at :33

scala> val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) schema: org.apache.spark.sql.types.StructType = StructType(StructField(C0,DoubleType,true), StructField(C1,DoubleType,true), StructField(C2,DoubleType,true), StructField(C3,DoubleType,true), StructField(C4,DoubleType,true), StructField(C5,DoubleType,true), StructField(C6,DoubleType,true), StructField(C7,DoubleType,true), StructField(C8,DoubleType,true), StructField(C9,DoubleType,true), StructField(C10,DoubleType,true), StructField(C11,DoubleType,true), StructField(C12,DoubleType,true), StructField(C13,DoubleType,true), StructField(C14,DoubleType,true), StructField(C15,DoubleType,true), StructField(C16,DoubleType,true), StructField(C17,DoubleType,true), StructField(C18,DoubleType,true), StructField(C19,DoubleType,true), StructField(C20,DoubleType,true), StructField(C21,DoubleType,true), ...

scala> val df = sqlContext.createDataFrame(data, schema) df: org.apache.spark.sql.DataFrame = [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, C8: double, C9: double, C10: double, C11: double, C12: double, C13: double, C14: double, C15: double, C16: double, C17: double, C18: double, C19: double, C20: double, C21: double, C22: double, C23: double, C24: double, C25: double, C26: double, C27: double, C28: double, C29: double, C30: double, C31: double, C32: double, C33: double, C34: double, C35: double, C36: double, C37: double, C38: double, C39: double, C40: double, C41: double, C42: double, C43: double, C44: double, C45: double, C46: double, C47: double, C48: double, C49: double, C50: double, C51: double, C52: double, C53: double, C54: double, C55: double, C56: double, C57: double, C58: double, C5...

{% endhighlight %}

Helper Methods

For convenience, we'll create some helper methods. The SystemML output data is encapsulated in an MLOutput object. The getScalar() method extracts a scalar value from a DataFrame returned by MLOutput. The getScalarDouble() method returns such a value as a Double, and the getScalarInt() method returns such a value as an Int.

scala> def getScalar(outputs: MLOutput, symbol: String): Any = | outputs.getDF(sqlContext, symbol).first()(1) getScalar: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Any

scala> def getScalarDouble(outputs: MLOutput, symbol: String): Double = | getScalar(outputs, symbol).asInstanceOf[Double] getScalarDouble: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Double

scala> def getScalarInt(outputs: MLOutput, symbol: String): Int = | getScalarDouble(outputs, symbol).toInt getScalarInt: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Int

{% endhighlight %}

{% endhighlight %}

Convert DataFrame to Binary-Block Matrix

SystemML is optimized to operate on a binary-block format for matrix representation. For large datasets, conversion from DataFrame to binary-block can require a significant quantity of time. Explicit DataFrame to binary-block conversion allows algorithm performance to be measured separately from data conversion time.

The SystemML binary-block matrix representation can be thought of as a two-dimensional array of blocks, where each block consists of a number of rows and columns. In this example, we specify a matrix consisting of blocks of size 1000x1000. The experimental dataFrameToBinaryBlock() method of RDDConverterUtilsExt is used to convert the DataFrame df to a SystemML binary-block matrix, which is represented by the datatype JavaPairRDD[MatrixIndexes, MatrixBlock].

scala> import org.apache.sysml.runtime.matrix.MatrixCharacteristics; import org.apache.sysml.runtime.matrix.MatrixCharacteristics

scala> val numRowsPerBlock = 1000 numRowsPerBlock: Int = 1000

scala> val numColsPerBlock = 1000 numColsPerBlock: Int = 1000

scala> val mc = new MatrixCharacteristics(numRows, numCols, numRowsPerBlock, numColsPerBlock) mc: org.apache.sysml.runtime.matrix.MatrixCharacteristics = [100000 x 1000, nnz=-1, blocks (1000 x 1000)]

scala> val sysMlMatrix = RDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc, false) sysMlMatrix: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@2bce3248

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DML Script

For this example, we will utilize the following DML Script called shape.dml that reads in a matrix and outputs the number of rows and the number of columns, each represented as a matrix.

{% highlight r %} X = read($Xin) m = matrix(nrow(X), rows=1, cols=1) n = matrix(ncol(X), rows=1, cols=1) write(m, $Mout) write(n, $Nout) {% endhighlight %}

Execute Script

Let's execute our DML script, as shown in the example below. The call to reset() of MLContext is not necessary here, but this method should be called if you need to reset inputs and outputs or if you would like to call execute() with a different script.

An example of registering the DataFrame df as an input to the X variable is shown but commented out. If a DataFrame is registered directly, it will implicitly be converted to SystemML‘s binary-block format. However, since we’ve already explicitly converted the DataFrame to the binary-block fixed variable systemMlMatrix, we will register this input to the X variable. We register the m and n variables as outputs.

When SystemML is executed via DMLScript (such as in Standalone Mode), inputs are supplied as either command-line named arguments or positional argument. These inputs are specified in DML scripts by prepending them with a $. Values are read from or written to files using read/write (DML) and load/save (PyDML) statements. When utilizing the MLContext API, inputs and outputs can be other data representations, such as DataFrames. The input and output data are bound to DML variables. The named arguments in the shape.dml script do not have default values set for them, so we create a Map to map the required named arguments to blank Strings so that the script can pass validation.

The shape.dml script is executed by the call to execute(), where we supply the Map of required named arguments. The execution results are returned as the MLOutput fixed variable outputs. The number of rows is obtained by calling the getStaticInt() helper method with the outputs object and "m". The number of columns is retrieved by calling getStaticInt() with outputs and "n".

scala> //ml.registerInput(“X”, df) // implicit conversion of DataFrame to binary-block

scala> ml.registerInput(“X”, sysMlMatrix, numRows, numCols)

scala> ml.registerOutput(“m”)

scala> ml.registerOutput(“n”)

scala> val nargs = Map(“Xin” -> " ", “Mout” -> " ", “Nout” -> " ") nargs: scala.collection.immutable.Map[String,String] = Map(Xin -> " ", Mout -> " ", Nout -> " ")

scala> val outputs = ml.execute(“shape.dml”, nargs) 15/10/12 16:29:15 WARN : Your hostname, derons-mbp.usca.ibm.com resolves to a loopback/non-reachable address: 127.0.0.1, but we couldn't find any external IP address! 15/10/12 16:29:15 WARN OptimizerUtils: Auto-disable multi-threaded text read for ‘text’ and ‘csv’ due to thread contention on JRE < 1.8 (java.version=1.7.0_80). outputs: org.apache.sysml.api.MLOutput = org.apache.sysml.api.MLOutput@4d424743

scala> val m = getScalarInt(outputs, “m”) m: Int = 100000

scala> val n = getScalarInt(outputs, “n”) n: Int = 1000

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DML Script as String

The MLContext API allows a DML script to be specified as a String. Here, we specify a DML script as a fixed String variable called minMaxMeanScript. This DML will find the minimum, maximum, and mean value of a matrix.

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Scala Wrapper for DML

We can create a Scala wrapper for our invocation of the minMaxMeanScript DML String. The minMaxMean() method takes a JavaPairRDD[MatrixIndexes, MatrixBlock] parameter, which is a SystemML binary-block matrix representation. It also takes a rows parameter indicating the number of rows in the matrix, a cols parameter indicating the number of columns in the matrix, and an MLContext parameter. The minMaxMean() method returns a tuple consisting of the minimum value in the matrix, the maximum value in the matrix, and the computed mean value of the matrix.

scala> import org.apache.sysml.runtime.matrix.data.MatrixBlock import org.apache.sysml.runtime.matrix.data.MatrixBlock

scala> import org.apache.spark.api.java.JavaPairRDD import org.apache.spark.api.java.JavaPairRDD

scala> def minMaxMean(mat: JavaPairRDD[MatrixIndexes, MatrixBlock], rows: Int, cols: Int, ml: MLContext): (Double, Double, Double) = { | ml.reset() | ml.registerInput(“Xin”, mat, rows, cols) | ml.registerOutput(“minOut”) | ml.registerOutput(“maxOut”) | ml.registerOutput(“meanOut”) | val outputs = ml.executeScript(minMaxMeanScript) | val minOut = getScalarDouble(outputs, “minOut”) | val maxOut = getScalarDouble(outputs, “maxOut”) | val meanOut = getScalarDouble(outputs, “meanOut”) | (minOut, maxOut, meanOut) | } minMaxMean: (mat: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock], rows: Int, cols: Int, ml: org.apache.sysml.api.MLContext)(Double, Double, Double)

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Invoking DML via Scala Wrapper

Here, we invoke minMaxMeanScript using our minMaxMean() Scala wrapper method. It returns a tuple consisting of the minimum value in the matrix, the maximum value in the matrix, and the mean value of the matrix.

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Zeppelin Notebook Example - Linear Regression Algorithm - OLD API

Next, we'll consider an example of a SystemML linear regression algorithm run from Spark through an Apache Zeppelin notebook. Instructions to clone and build Zeppelin can be found at the GitHub Apache Zeppelin site. This example also will look at the Spark ML linear regression algorithm.

This Zeppelin notebook example can be imported by choosing Import note -> Add from URL from the Zeppelin main page, then insert the following URL:

https://raw.githubusercontent.com/apache/incubator-systemml/master/samples/zeppelin-notebooks/2AZ2AQ12B/note.json

Alternatively download note.json, then import it by choosing Import note -> Choose a JSON here from the Zeppelin main page.

A conf/zeppelin-env.sh file is created based on conf/zeppelin-env.sh.template. For this demonstration, it features SPARK_HOME, SPARK_SUBMIT_OPTIONS, and ZEPPELIN_SPARK_USEHIVECONTEXT environment variables:

export SPARK_HOME=/Users/example/spark-1.5.1-bin-hadoop2.6
export SPARK_SUBMIT_OPTIONS="--jars /Users/example/systemml/system-ml/target/SystemML.jar"
export ZEPPELIN_SPARK_USEHIVECONTEXT=false

Start Zeppelin using the zeppelin.sh script:

bin/zeppelin.sh

After opening Zeppelin in a brower, we see the “SystemML - Linear Regression” note in the list of available Zeppelin notes.

Zeppelin Notebook

If we go to the “SystemML - Linear Regression” note, we see that the note consists of several cells of code.