Use the SageMakerEstimator in a Spark Pipeline

You can use org.apache.spark.ml.Estimator estimators and org.apache.spark.ml.Model models, and SageMakerEstimator estimators and SageMakerModel models in org.apache.spark.ml.Pipeline pipelines, as shown in the following example:

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.PCA
import org.apache.spark.sql.SparkSession
import com.amazonaws.services.sagemaker.sparksdk.IAMRole
import com.amazonaws.services.sagemaker.sparksdk.algorithms
import com.amazonaws.services.sagemaker.sparksdk.algorithms.KMeansSageMakerEstimator

val spark = SparkSession.builder.getOrCreate

// load mnist data as a dataframe from libsvm 
val region = "us-east-1"
val trainingData = spark.read.format("libsvm")
  .option("numFeatures", "784")
  .load(s"s3://sagemaker-sample-data-$region/spark/mnist/train/")
val testData = spark.read.format("libsvm")
  .option("numFeatures", "784")
  .load(s"s3://sagemaker-sample-data-$region/spark/mnist/test/")

// substitute your SageMaker IAM role here
val roleArn = "arn:aws:iam::account-id:role/rolename"

val pcaEstimator = new PCA()
  .setInputCol("features")
  .setOutputCol("projectedFeatures")
  .setK(50)

val kMeansSageMakerEstimator = new KMeansSageMakerEstimator(
  sagemakerRole = IAMRole(integTestingRole),
  requestRowSerializer =
    new ProtobufRequestRowSerializer(featuresColumnName = "projectedFeatures"),
  trainingSparkDataFormatOptions = Map("featuresColumnName" -> "projectedFeatures"),
  trainingInstanceType = "ml.p2.xlarge",
  trainingInstanceCount = 1,
  endpointInstanceType = "ml.c4.xlarge",
  endpointInitialInstanceCount = 1)
  .setK(10).setFeatureDim(50)

val pipeline = new Pipeline().setStages(Array(pcaEstimator, kMeansSageMakerEstimator))

// train
val pipelineModel = pipeline.fit(trainingData)

val transformedData = pipelineModel.transform(testData)
transformedData.show()

The parameter trainingSparkDataFormatOptions configures Spark to serialize to protobuf the “projectedFeatures” column for model training. Additionally, Spark serializes to protobuf the “label” column by default.

Because we want to make inferences using the “projectedFeatures” column, we pass the column name into the ProtobufRequestRowSerializer.

The following example shows a transformed DataFrame:

+-----+--------------------+--------------------+-------------------+---------------+
|label|            features|   projectedFeatures|distance_to_cluster|closest_cluster|
+-----+--------------------+--------------------+-------------------+---------------+
|  5.0|(784,[152,153,154...|[880.731433034386...|     1500.470703125|            0.0|
|  0.0|(784,[127,128,129...|[1768.51722024166...|      1142.18359375|            4.0|
|  4.0|(784,[160,161,162...|[704.949236329314...|  1386.246826171875|            9.0|
|  1.0|(784,[158,159,160...|[-42.328192193771...| 1277.0736083984375|            5.0|
|  9.0|(784,[208,209,210...|[374.043902028333...|   1211.00927734375|            3.0|
|  2.0|(784,[155,156,157...|[941.267714528850...|  1496.157958984375|            8.0|
|  1.0|(784,[124,125,126...|[30.2848596410594...| 1327.6766357421875|            5.0|
|  3.0|(784,[151,152,153...|[1270.14374062052...| 1570.7674560546875|            0.0|
|  1.0|(784,[152,153,154...|[-112.10792566485...|     1037.568359375|            5.0|
|  4.0|(784,[134,135,161...|[452.068280676606...| 1165.1236572265625|            3.0|
|  3.0|(784,[123,124,125...|[610.596447285397...|  1325.953369140625|            7.0|
|  5.0|(784,[216,217,218...|[142.959601818422...| 1353.4930419921875|            5.0|
|  3.0|(784,[143,144,145...|[1036.71862533658...| 1460.4315185546875|            7.0|
|  6.0|(784,[72,73,74,99...|[996.740157435754...| 1159.8631591796875|            2.0|
|  1.0|(784,[151,152,153...|[-107.26076167417...|   960.963623046875|            5.0|
|  7.0|(784,[211,212,213...|[619.771820430940...|   1245.13623046875|            6.0|
|  2.0|(784,[151,152,153...|[850.152101817161...|  1304.437744140625|            8.0|
|  8.0|(784,[159,160,161...|[370.041887230547...| 1192.4781494140625|            0.0|
|  6.0|(784,[100,101,102...|[546.674328209335...|    1277.0908203125|            2.0|
|  9.0|(784,[209,210,211...|[-29.259112927426...| 1245.8182373046875|            6.0|
+-----+--------------------+--------------------+-------------------+---------------+