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SageMaker

SageMaker Spark

codecov

SageMaker Spark is an open source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting.

With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale.

Table of Contents

Getting SageMaker Spark

Scala

SageMaker Spark for Scala is available in the Maven central repository:

<dependency>
    <groupId>com.amazonaws</groupId>
    <artifactId>sagemaker-spark_2.11</artifactId>
    <version>spark_2.2.0-1.0</version>
</dependency>

Or, if your project depends on Spark 2.1:

<dependency>
    <groupId>com.amazonaws</groupId>
    <artifactId>sagemaker-spark_2.11</artifactId>
    <version>spark_2.1.1-1.0</version>
</dependency>

You can also build SageMaker Spark from source. See sagemaker-spark-sdk for more on building SageMaker Spark from source.

Python

See the sagemaker-pyspark-sdk for more on installing and running SageMaker PySpark.

Running SageMaker Spark

SageMaker Spark depends on hadoop-aws-2.8.1. To run Spark applications that depend on SageMaker Spark, you need to build Spark with Hadoop 2.8. However, if you are running Spark applications on EMR, you can use Spark built with Hadoop 2.7.

Apache Spark currently distributes binaries built against Hadoop-2.7, but not 2.8. See the Spark documentation for more on building Spark with Hadoop 2.8.

SageMaker Spark needs to be added to both the driver and executor classpaths.

Running SageMaker Spark Applications with spark-shell or spark-submit

You can submit SageMaker Spark and the AWS Java Client as dependencies with the "--jars" flag, or take a dependency on SageMaker Spark in Maven using the "--package" flag.

  1. Install Hadoop-2.8. https://hadoop.apache.org/docs/r2.8.0/
  2. Build Spark 2.2 with Hadoop-2.8. The Spark documentation has guidance on building Spark with your own Hadoop installation.
  3. Run spark-shell or spark-submit with the --packages flag:
spark-shell --packages com.amazonaws:sagemaker-spark_2.11:spark_2.2.0-1.0

Running SageMaker Spark Applications on EMR

You can run SageMaker Spark applications on an EMR cluster just like any other Spark application by submitting your Spark application jar and the SageMaker Spark dependency jars with the --jars or --packages flags.

SageMaker Spark is pre-installed on EMR releases since 5.11.0. You can run your SageMaker Spark application on EMR by submitting your Spark application jar and any additional dependencies your Spark application uses.

SageMaker Spark applications have also been verified to be compatible with EMR-5.6.0 (which runs Spark 2.1) and EMR-5-8.0 (which runs Spark 2.2). When submitting your Spark application to an earlier EMR release, use the --packages flag to depend on a recent version of the AWS Java SDK:

spark-submit
  --packages com.amazonaws:aws-java-sdk:1.11.238 \
  --deploy-mode cluster \
  --conf spark.driver.userClassPathFirst=true \
  --conf spark.executor.userClassPathFirst=true \
  --jars SageMakerSparkApplicationJar.jar,...
  ...

The spark.driver.userClassPathFirst=true and spark.executor.userClassPathFirst=true properties are required so that the Spark cluster will use the AWS Java SDK dependencies with SageMaker, rather than the AWS Java SDK installed on these earlier EMR clusters.

For more on running Spark application on EMR, see the EMR Documentation on submitting a step.

Python

See the sagemaker-pyspark-sdk for more on installing and running SageMaker PySpark.

S3 FileSystem Schemes

EMR allows you to read and write data using the EMR FileSystem (EMRFS), accessed through Spark with "s3://":

spark.read.format("libsvm").load("s3://my-bucket/my-prefix")

In other execution environments, you can use the S3A schema to use the S3A FileSystem "s3a://" to read and write data:

spark.read.format("libsvm").load("s3a://my-bucket/my-prefix")

In the code examples in this README, we use "s3://" to use the EMRFS, or "s3a://" to use the S3A system, which is recommended over "s3n://".

API Documentation

You can view the Scala API Documentation for SageMaker Spark here.

You can view the PySpark API Documentation for SageMaker Spark here.

Getting Started: K-Means Clustering on SageMaker with SageMaker Spark SDK

This example walks through using SageMaker Spark to train on a Spark DataFrame using a SageMaker-provided algorithm, host the resulting model on SageMaker Spark, and making predictions on a Spark DataFrame using that hosted model.

We'll cluster handwritten digits in the MNIST dataset, which we've made available in LibSVM format at s3://sagemaker-sample-data-us-east-1/spark/mnist/train/mnist_train.libsvm.

You can start a Spark shell with SageMaker Spark

spark-shell --packages com.amazonaws:sagemaker-spark_2.11:spark_2.1.1-1.0
  1. Create your Spark Session and load your training and test data into DataFrames:
val spark = SparkSession.builder.getOrCreate

// load mnist data as a dataframe from libsvm. replace this region with your own.
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/")

The DataFrame consists of a column named "label" of Doubles, indicating the digit for each example, and a column named "features" of Vectors:

trainingData.show

+-----+--------------------+
|label|            features|
+-----+--------------------+
|  5.0|(784,[152,153,154...|
|  0.0|(784,[127,128,129...|
|  4.0|(784,[160,161,162...|
|  1.0|(784,[158,159,160...|
|  9.0|(784,[208,209,210...|
|  2.0|(784,[155,156,157...|
|  1.0|(784,[124,125,126...|
|  3.0|(784,[151,152,153...|
|  1.0|(784,[152,153,154...|
|  4.0|(784,[134,135,161...|
|  3.0|(784,[123,124,125...|
|  5.0|(784,[216,217,218...|
|  3.0|(784,[143,144,145...|
|  6.0|(784,[72,73,74,99...|
|  1.0|(784,[151,152,153...|
|  7.0|(784,[211,212,213...|
|  2.0|(784,[151,152,153...|
|  8.0|(784,[159,160,161...|
|  6.0|(784,[100,101,102...|
|  9.0|(784,[209,210,211...|
+-----+--------------------+
  1. Construct a KMeansSageMakerEstimator, which extends SageMakerEstimator, which is a Spark Estimator. You need to pass in an Amazon SageMaker-compatible IAM Role that Amazon SageMaker will use to make AWS service calls on your behalf (or configure SageMaker Spark to get this from Spark Config). Consult the API Documentation for a complete list of parameters.

In this example, we are setting the "k" and "feature_dim" hyperparameters, corresponding to the number of clusters we want and to the number of dimensions in our training dataset, respectively.

// Replace this IAM Role ARN with your own.
val roleArn = "arn:aws:iam::account-id:role/rolename"

val estimator = new KMeansSageMakerEstimator(
  sagemakerRole = IAMRole(roleArn),
  trainingInstanceType = "ml.p2.xlarge",
  trainingInstanceCount = 1,
  endpointInstanceType = "ml.c4.xlarge",
  endpointInitialInstanceCount = 1)
  .setK(10).setFeatureDim(784)
  1. To train and host your model, call fit() on your training DataFrame:
val model = estimator.fit(trainingData)

What happens in this call to fit()?

  1. SageMaker Spark serializes your DataFrame and uploads the serialized training data to S3. For the K-Means algorithm, SageMaker Spark converts the DataFrame to the Amazon Record format. SageMaker Spark will create an S3 bucket for you that your IAM role can access if you do not provide an S3 Bucket in the constructor.

  2. SageMaker Spark sends a CreateTrainingJobRequest to Amazon SageMaker to run a Training Job with one p2.xlarge on the data in S3, configured with the values you pass in to the SageMakerEstimator, and polls for completion of the Training Job. In this example, we are sending a CreateTrainingJob request to run a k-means clustering Training Job on Amazon SageMaker on serialized data we uploaded from your DataFrame. When training completes, the Amazon SageMaker service puts a serialized model in an S3 bucket you own (or the default bucket created by SageMaker Spark).

  3. After training completes, SageMaker Spark sends a CreateModelRequest, a CreateEndpointConfigRequest, and a CreateEndpointRequest and polls for completion, each configured with the values you pass in to the SageMakerEstimator. This Endpoint will initially be backed by one c4.xlarge.

  4. To make inferences using the Endpoint hosting our model, call transform() on the SageMakerModel returned by fit().

val transformedData = model.transform(testData)
transformedData.show
+-----+--------------------+-------------------+---------------+
|label|            features|distance_to_cluster|closest_cluster|
+-----+--------------------+-------------------+---------------+
|  5.0|(784,[152,153,154...|  1767.897705078125|            4.0|
|  0.0|(784,[127,128,129...|  1392.157470703125|            5.0|
|  4.0|(784,[160,161,162...| 1671.5711669921875|            9.0|
|  1.0|(784,[158,159,160...| 1182.6082763671875|            6.0|
|  9.0|(784,[208,209,210...| 1390.4002685546875|            0.0|
|  2.0|(784,[155,156,157...|  1713.988037109375|            1.0|
|  1.0|(784,[124,125,126...| 1246.3016357421875|            2.0|
|  3.0|(784,[151,152,153...|  1753.229248046875|            4.0|
|  1.0|(784,[152,153,154...|  978.8394165039062|            2.0|
|  4.0|(784,[134,135,161...|  1623.176513671875|            3.0|
|  3.0|(784,[123,124,125...|  1533.863525390625|            4.0|
|  5.0|(784,[216,217,218...|  1469.357177734375|            6.0|
|  3.0|(784,[143,144,145...|  1736.765869140625|            4.0|
|  6.0|(784,[72,73,74,99...|   1473.69384765625|            8.0|
|  1.0|(784,[151,152,153...|    944.88720703125|            2.0|
|  7.0|(784,[211,212,213...| 1285.9071044921875|            3.0|
|  2.0|(784,[151,152,153...| 1635.0125732421875|            1.0|
|  8.0|(784,[159,160,161...| 1436.3162841796875|            6.0|
|  6.0|(784,[100,101,102...| 1499.7366943359375|            7.0|
|  9.0|(784,[209,210,211...| 1364.6319580078125|            6.0|
+-----+--------------------+-------------------+---------------+

In this call to transform(), the SageMakerModel serializes chunks of the input DataFrame and sends them to the Endpoint using the SageMakerRuntime InvokeEndpoint API. The SageMakerModel deserializes the Endpoint's responses, which contain predictions, and appends the prediction columns to the input DataFrame.

Example: Using SageMaker Spark with Any SageMaker Algorithm

The SageMakerEstimator is an org.apache.spark.ml.Estimator that trains a model on Amazon SageMaker.

SageMaker Spark provides several classes that extend SageMakerEstimator to run particular algorithms, like KMeansSageMakerEstimator to run the SageMaker-provided k-means algorithm, or XGBoostSageMakerEstimator to run the SageMaker-provided XGBoost algorithm. These classes are just SageMakerEstimators with certain default values passed in. You can use SageMaker Spark with any algorithm that runs on Amazon SageMaker by creating a SageMakerEstimator.

Instead of creating a KMeansSageMakerEstimator, you can create an equivalent SageMakerEstimator:

val estimator = new SageMakerEstimator(
  trainingImage =
    "382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1",
  modelImage =
    "382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1",
  requestRowSerializer = new ProtobufRequestRowSerializer(),
  responseRowDeserializer = new KMeansProtobufResponseRowDeserializer(),
  hyperParameters = Map("k" -> "10", "feature_dim" -> "784"),
  sagemakerRole = IAMRole(roleArn),
  trainingInstanceType = "ml.p2.xlarge",
  trainingInstanceCount = 1,
  endpointInstanceType = "ml.c4.xlarge",
  endpointInitialInstanceCount = 1,
  trainingSparkDataFormat = "sagemaker")
  • trainingImage identifies the Docker registry path to the training image containing your custom code. In this case, this points to the us-east-1 k-means image.
  • modelImage identifies the Docker registry path to the image containing inference code. Amazon SageMaker k-means uses the same image to train and to host trained models.
  • requestRowSerializer implements com.amazonaws.services.sagemaker.sparksdk.transformation.RequestRowSerializer. A RequestRowSerializer serializes org.apache.spark.sql.Rows in the input DataFrame to send them to the model hosted in Amazon SageMaker for inference. This is passed to the SageMakerModel returned by fit. In this case, we pass in a RequestRowSerializer that serializes Rows to the Amazon Record protobuf format. See Serializing and Deserializing for Inference for more information on how SageMaker Spark makes inferences.
  • responseRowDeserializer Implements com.amazonaws.services.sagemaker.sparksdk.transformation.ResponseRowDeserializer. A ResponseRowDeserializer deserializes responses containing predictions from the Endpoint back into columns in a DataFrame.
  • hyperParameters is a Map[String, String] that the trainingImage will use to set training hyperparameters.
  • trainingSparkDataFormat specifies the data format that Spark uses when uploading training data from a DataFrame to S3.

SageMaker Spark needs the trainingSparkDataFormat to tell Spark how to write the DataFrame to S3 for the trainingImage to train on. In this example, "sagemaker" tells Spark to write the data as RecordIO-encoded Amazon Records, but your own algorithm may take another data format. You can pass in any format that Spark supports as long as your trainingImage can train using that data format, such as "csv", "parquet", "com.databricks.spark.csv", or "libsvm."

SageMaker Spark also needs a RequestRowSerializer to serialize Spark Rows to a data format the modelImage can deserialize, and a ResponseRowDeserializer to deserialize responses that contain predictions from the modelImage back into Spark Rows. See Serializing and Deserializing for Inference for more details.

Example: Using SageMakerEstimator and SageMakerModel in a Spark Pipeline

SageMakerEstimators and SageMakerModels can be used in Pipelines. In this example, we run org.apache.spark.ml.feature.PCA on our Spark cluster, then train and infer using Amazon SageMaker's K-Means on the output column from PCA:

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

val kMeansSageMakerEstimator = new KMeansSageMakerEstimator(
  sagemakerRole = IAMRole(roleArn),
  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()

+-----+--------------------+--------------------+-------------------+---------------+
|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|
+-----+--------------------+--------------------+-------------------+---------------+
  • requestRowSerializer = new ProtobufRequestRowSerializer(featuresColumnName = "projectedFeatures") tells the SageMakerModel returned by fit() to infer on the features in the "projectedFeatures" column
  • trainingSparkDataFormatOptions = Map("featuresColumnName" -> "projectedFeatures") tells the SageMakerProtobufWriter that Spark is using to write the DataFrame as format "sagemaker" to serialize the "projectedFeatures" column when writing Amazon Records for training.

Example: Using Multiple SageMakerEstimators and SageMakerModels in a Spark Pipeline

We can use multiple SageMakerEstimators and SageMakerModels in a pipeline. Here, we use SageMaker's PCA algorithm to reduce a dataset with 50 dimensions to a dataset with 20 dimensions, then use SageMaker's K-Means algorithm to train on the 20-dimension data.

val pcaEstimator = new PCASageMakerEstimator(sagemakerRole = IAMRole(sagemakerRole),
  trainingInstanceType = "ml.p2.xlarge",
  trainingInstanceCount = 1,
  endpointInstanceType = "ml.c4.xlarge",
  endpointInitialInstanceCount = 1
  responseRowDeserializer = new PCAProtobufResponseRowDeserializer(
    projectionColumnName = "projectionDim20"),
  trainingInputS3DataPath = S3DataPath(trainingBucket, inputPrefix),
  trainingOutputS3DataPath = S3DataPath(trainingBucket, outputPrefix),
  endpointCreationPolicy = EndpointCreationPolicy.CREATE_ON_TRANSFORM)
  .setNumComponents(20).setFeatureDim(50)

val kmeansEstimator = new KMeansSageMakerEstimator(sagemakerRole = IAMRole(sagemakerRole),
  trainingInstanceType = "ml.p2.xlarge",
  trainingInstanceCount = 1,
  endpointInstanceType = "ml.c4.xlarge",
  endpointInitialInstanceCount = 1
  trainingSparkDataFormatOptions = Map("featuresColumnName" -> "projectionDim20"),
  requestRowSerializer = new ProtobufRequestRowSerializer(
    featuresColumnName = "projectionDim20"),
  responseRowDeserializer = new KMeansProtobufResponseRowDeserializer(),
  trainingInputS3DataPath = S3DataPath(trainingBucket, inputPrefix),
  trainingOutputS3DataPath = S3DataPath(trainingBucket, outputPrefix),
  endpointCreationPolicy = EndpointCreationPolicy.CREATE_ON_TRANSFORM)
  .setK(10).setFeatureDim(20)

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

val model = pipeline.fit(dataset)

// For expediency, transforming the training dataset:
val transformedData = model.transform(dataset)
transformedData.show()

+-----+--------------------+--------------------+-------------------+---------------+
|label|            features|     projectionDim20|distance_to_cluster|closest_cluster|
+-----+--------------------+--------------------+-------------------+---------------+
|  1.0|[-0.7927307,-11.2...|[5.50362682342529...|  45.03189468383789|            1.0|
|  1.0|[-3.762671,-5.853...|[-2.1558122634887...|  41.79889678955078|            1.0|
|  1.0|[-2.0988898,-2.40...|[4.53881502151489...| 50.824703216552734|            1.0|
|  1.0|[-2.81075,-3.6481...|[0.97894239425659...|  52.78211975097656|            1.0|
|  1.0|[-2.14356,-4.0369...|[2.25758934020996...|  48.99141311645508|            1.0|
|  1.0|[-5.3773708,-15.3...|[-3.2523036003112...|  21.99374771118164|            1.0|
|  1.0|[-1.0369565,-16.5...|[-17.643878936767...| 29.127044677734375|            3.0|
|  1.0|[-2.019725,-3.226...|[1.41068196296691...|   51.7830696105957|            1.0|
|  1.0|[-4.3821997,-0.98...|[-0.8335087299346...| 53.921058654785156|            1.0|
|  1.0|[-7.075208,-34.31...|[11.4329795837402...|  35.12031173706055|            3.0|
|  1.0|[-3.90454,-4.8401...|[-1.4304646253585...|  50.00594711303711|            1.0|
|  1.0|[0.9607103,-13.50...|[1.13785743713378...|  28.71956443786621|            1.0|
|  1.0|[-4.5025017,-15.2...|[2.66747045516967...| 25.419822692871094|            1.0|
|  1.0|[0.041773,-27.148...|[7.58121681213378...| 30.303693771362305|            3.0|
|  1.0|[-10.1477266,-39....|[-12.086886405944...|   35.9030647277832|            2.0|
|  1.0|[-3.09143,-6.4892...|[1.79180252552032...|  39.34271240234375|            1.0|
|  1.0|[-13.5285917,-32....|[7.62783145904541...| 35.040035247802734|            2.0|
|  1.0|[-4.189806,-16.04...|[1.41141772270202...| 25.123626708984375|            1.0|
|  1.0|[-12.77831508,-62...|[0.11281073093414...|  63.91242599487305|            2.0|
|  1.0|[-9.3934507,-12.5...|[-9.4945802688598...| 20.913305282592773|            1.0|
+-----+--------------------+--------------------+-------------------+---------------+
  • responseRowDeserializer = new PCAProtobufResponseRowDeserializer( projectionColumnName = "projectionDim20") tells the SageMakerModel attached to the PCA endpoint to deserialize responses (which contain the lower-dimensional projections of the features vectors) into the column named "projectionDim20"
  • endpointCreationPolicy = EndpointCreationPolicy.CREATE_ON_TRANSFORM tells the SageMakerEstimator to delay SageMaker Endpoint creation until it is needed to transform a DataFrame.
  • trainingSparkDataFormatOptions = Map("featuresColumnName" -> "projectionDim20"), requestRowSerializer = new ProtobufRequestRowSerializer( featuresColumnName = "projectionDim20") these lines tell the KMeansSageMakerEstimator to respectively train and infer on the features in the "projectionDim20" column.

Example: Creating a SageMakerModel

SageMaker Spark supports attaching SageMakerModels to an existing SageMaker endpoint, or to an Endpoint created by reference to model data in S3, or to a previously completed Training Job.

This allows you to use SageMaker Spark just for model hosting and inference on Spark-scale DataFrames without running a new Training Job.

SageMakerModel From an Endpoint

You can attach a SageMakerModel to an endpoint that has already been created. Supposing an endpoint with name "my-endpoint-name" is already in service and hosting a SageMaker K-Means model:

val model = SageMakerModel
  .fromEndpoint(endpointName = "my-endpoint-name",
                requestRowSerializer = new ProtobufRequestRowSerializer(
                  featuresColumnName = "MyFeaturesColumn"),
                responseRowDeserializer = new KMeansProtobufResponseRowDeserializer(
                  distanceToClusterColumnName = "DistanceToCluster",
                  closestClusterColumnName = "ClusterLabel"
                ))

This SageMakerModel will, upon a call to transform(), serialize the column named "MyFeaturesColumn" for inference, and append the columns "DistanceToCluster" and "ClusterLabel" to the DataFrame.

SageMakerModel From Model Data in S3

You can create a SageMakerModel and an Endpoint by referring directly to your model data in S3:

val model = SageMakerModel
  .fromModelS3Path(modelPath = "s3://my-model-bucket/my-model-data/model.tar.gz",
                   modelExecutionRoleARN = "arn:aws:iam::account-id:role/rolename"
                   modelImage = 382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1",
                   endpointInstanceType = "ml.c4.xlarge",
                   endpointInitialInstanceCount = 1
                   requestRowSerializer = new ProtobufRequestRowSerializer(),
                   responseRowDeserializer = new KMeansProtobufResponseRowDeserializer()
                  )

SageMakerModel From a Previously Completed Training Job

You can create a SageMakerModel and an Endpoint by referring to a previously-completed training job:

val model = SageMakerModel
  .fromTrainingJob(trainingJobName = "my-training-job-name",
                   modelExecutionRoleARN = "arn:aws:iam::account-id:role/rolename"
                   modelImage = 382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1",
                   endpointInstanceType = "ml.c4.xlarge",
                   endpointInitialInstanceCount = 1
                   requestRowSerializer = new ProtobufRequestRowSerializer(),
                   responseRowDeserializer = new KMeansProtobufResponseRowDeserializer()
                  )

Example: Tearing Down Amazon SageMaker Endpoints

SageMaker Spark provides a utility for deleting Endpoints created by a SageMakerModel:

val sagemakerClient = AmazonSageMakerClientBuilder.defaultClient
val cleanup = new SageMakerResourceCleanup(sagemakerClient)
cleanup.deleteResources(model.getCreatedResources)

Configuring an IAM Role

SageMaker Spark allows you to add your IAM Role ARN to your Spark Config so that you don't have to keep passing in IAMRole("arn:aws:iam::account-id:role/rolename").

Add an entry to your Spark Config with key com.amazonaws.services.sagemaker.sparksdk.sagemakerrole whose value is your Amazon SageMaker-compatible IAM Role. SageMakerEstimator will look for this role if it is not supplied in the constructor.

SageMaker Spark: In-Depth

The Amazon Record format

KMeansSageMakerEstimator, PCASageMakerEstimator, and LinearLearnerSageMakerEstimator all serialize DataFrames to the Amazon Record protobuf format with each Record encoded in RecordIO. They do this by passing in "sagemaker" to the trainingSparkDataFormat constructor argument, which configures Spark to use the SageMakerProtobufWriter to serialize Spark DataFrames.

Writing a DataFrame using the "sagemaker" format serializes a column named "label", expected to contain Doubles, and a column named "features", expected to contain a Sparse or Dense org.apache.mllib.linalg.Vector. If the features column contains a SparseVector, SageMaker Spark sparsely-encodes the Vector into the Amazon Record. If the features column contains a DenseVector, SageMaker Spark densely-encodes the Vector into the Amazon Record.

You can choose which columns the SageMakerEstimator chooses as its "label" and "features" columns by passing in a trainingSparkDataFormatOptions Map[String, String] with keys "labelColumnName" and "featuresColumnName" and with values corresponding to the names of your chosen label and features columns.

You can also write Amazon Records using SageMaker Spark by using the "sagemaker" format directly:

myDataFrame.write
    .format("sagemaker")
    .option("labelColumnName", "myLabelColumn")
    .option("featuresColumnName", "myFeaturesColumn")
    .save("s3://my-s3-bucket/my-s3-prefix")

By default, SageMakerEstimator deletes the RecordIO-encoded Amazon Records in S3 following training on Amazon SageMaker. You can choose to allow the data to persist in S3 by passing in deleteStagingDataAfterTraining = true to SageMakerEstimator.

See the AWS Documentation on Amazon Records for more information on Amazon Records.

Serializing and Deserializing for Inference

SageMakerEstimator.fit() returns a SageMakerModel, which transforms a DataFrame by calling InvokeEndpoint on an Amazon SageMaker Endpoint. InvokeEndpointRequests carry serialized Rows as their payload.Rows in the DataFrame are serialized for predictions against an Endpoint using a RequestRowSerializer. Responses from an Endpoint containing predictions are deserialized into Spark Rows and appended as columns in a DataFrame using a ResponseRowDeserializer.

Internally, SageMakerModel.transform calls mapPartitions to distribute the work of serializing Spark Rows, constructing and sending InvokeEndpointRequests to an Endpoint, and deserializing InvokeEndpointResponses across a Spark cluster. Because each InvokeEndpointRequest can carry only 5MB, each Spark partition creates a com.amazonaws.services.sagemaker.sparksdk.transformation.util.RequestBatchIterator to iterate over its partition, sending prediction requests to the Endpoint in 5MB increments.

RequestRowSerializer.serializeRow() converts a Row to an Array[Byte]. The RequestBatchIterator appends these byte arrays to form the request body of an InvokeEndpointRequest.

For example, the com.amazonaws.services.sagemaker.sparksdk.transformation.ProtobufRequestRowSerializer creates one RecordIO-encoded Amazon Record per input row by serializing the "features" column in each row, and wrapping each Amazon Record in the RecordIO header.

ResponseRowDeserializer.deserializeResponse() converts an Array[Byte] containing predictions from an Endpoint to an Iterator[Row]to appends columns containing these predictions to the DataFrame being transformed by the SageMakerModel.

For comparison, SageMaker's XGBoost uses LibSVM-formatted data for inference (as well as training), and responds with a comma-delimited list of predictions. Accordingly, SageMaker Spark uses com.amazonaws.services.sagemaker.sparksdk.transformation.LibSVMRequestRowSerializer to serialize rows into LibSVM-formatted data, and uses com.amazonaws.services.sagemaker.sparksdk.transformation.XGBoostCSVResponseRowDeserializer to deserialize the response into a column of predictions.

To support your own model image's data formats for inference, you can implement your own RequestRowSerializer and ResponseRowDeserializer.

License

SageMaker Spark is licensed under Apache-2.0.

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