기계 번역으로 제공되는 번역입니다. 제공된 번역과 원본 영어의 내용이 상충하는 경우에는 영어 버전이 우선합니다.

# Spark 파이프라인에서 SageMakerEstimator 사용
<a name="apache-spark-example1-extend-pipeline"></a>

다음 예제에서 표시된 것과 같이 `org.apache.spark.ml.Estimator`예측기, `org.apache.spark.ml.Model`모델, `SageMakerEstimator`예측기 및 `SageMakerModel`모델을 `org.apache.spark.ml.Pipeline`파이프라인에서 사용할 수 있습니다.

```
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()
```

파라미터 `trainingSparkDataFormatOptions`는 Spark가 모델 훈련을 위해 "projectedFeatures" 열을 protobuf로 직렬화하도록 Spark를 구성합니다. 추가로 Spark는 기본적으로 protobuf "label" 열로 직렬화합니다.

"projectedFeatures" 열을 사용하여 추론을 수행하기 위해 열 이름을 `ProtobufRequestRowSerializer`에 전달합니다.

다음 예제는 변형된 `DataFrame`을 보여줍니다.

```
+-----+--------------------+--------------------+-------------------+---------------+
|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|
+-----+--------------------+--------------------+-------------------+---------------+
```