Cannot use vector as input struct type due to: java.lang.ClassCastException: scala.collection.convert.Wrappers$JListWrapper cannot be cast to ml.combust.mleap.tensor.Tensor

[make]

I am trying to deploy bundled a Spark ML NaiveBayesModel with sagemaker-sparkml-serving-container.

I am running sagemaker-sparkml-serving-container with following command:

SCHEMA='{"input":[{"name":"features","type":"double","struct":"vector"}],"output":{"name":"prediction","type":"double"}}'
BUNDLE=/tmp/naivebayes_bundle
docker run -p 8080:8080 -e SAGEMAKER_SPARKML_SCHEMA="$SCHEMA" -v $BUNDLE:/opt/ml/model sagemaker-sparkml-serving:2.2 serve

When calling /invocations with:

curl -i -H "content-type:application/json" http://localhost:8080/invocations -d '{"data":[[1.0,2.0,3.0]]}'

Following error is thrown:

java.lang.ClassCastException: scala.collection.convert.Wrappers$JListWrapper cannot be cast to ml.combust.mleap.tensor.Tensor
	at ml.combust.mleap.runtime.transformer.classification.NaiveBayesClassifier$$anonfun$1.apply(NaiveBayesClassifier.scala:19) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.Row$class.udfValue(Row.scala:241) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.ArrayRow.udfValue(ArrayRow.scala:17) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.Row$class.withValues(Row.scala:225) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.ArrayRow.withValues(ArrayRow.scala:17) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame$$anonfun$withColumns$1$$anonfun$apply$3$$anonfun$4.apply(DefaultLeapFrame.scala:79) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame$$anonfun$withColumns$1$$anonfun$apply$3$$anonfun$4.apply(DefaultLeapFrame.scala:79) ~[sparkml-serving-2.2.jar:2.2]
	at scala.collection.immutable.Stream.map(Stream.scala:418) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame$$anonfun$withColumns$1$$anonfun$apply$3.apply(DefaultLeapFrame.scala:79) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame$$anonfun$withColumns$1$$anonfun$apply$3.apply(DefaultLeapFrame.scala:78) ~[sparkml-serving-2.2.jar:2.2]
	at scala.util.Success$$anonfun$map$1.apply(Try.scala:237) ~[sparkml-serving-2.2.jar:2.2]
	at scala.util.Try$.apply(Try.scala:192) ~[sparkml-serving-2.2.jar:2.2]
	at scala.util.Success.map(Try.scala:237) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame$$anonfun$withColumns$1.apply(DefaultLeapFrame.scala:77) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame$$anonfun$withColumns$1.apply(DefaultLeapFrame.scala:72) ~[sparkml-serving-2.2.jar:2.2]
	at scala.util.Success.flatMap(Try.scala:231) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.DefaultLeapFrame.withColumns(DefaultLeapFrame.scala:71) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.frame.MultiTransformer$class.transform(Transformer.scala:121) ~[sparkml-serving-2.2.jar:2.2]
	at ml.combust.mleap.runtime.transformer.classification.NaiveBayesClassifier.transform(NaiveBayesClassifier.scala:13) ~[sparkml-serving-2.2.jar:2.2]
	at com.amazonaws.sagemaker.utils.ScalaUtils.transformLeapFrame(ScalaUtils.java:44) ~[sparkml-serving-2.2.jar:2.2]
	at com.amazonaws.sagemaker.controller.ServingController.processInputData(ServingController.java:176) ~[sparkml-serving-2.2.jar:2.2]
	at com.amazonaws.sagemaker.controller.ServingController.transformRequestJson(ServingController.java:118) ~[sparkml-serving-2.2.jar:2.2]

Created bundle with following dependencies:

org.apache.spark:spark-core_2.11:2.4.0
org.apache.spark:spark-mllib_2.11:2.4.0
ml.combust.mleap:mleap-spark_2.11:0.12.0

Kotlin code that creates the bundle:

val model = NaiveBayes()
        .setModelType("multinomial")
        .fit(data)
SimpleSparkSerializer().serializeToBundle(model, "file:/tmp/naivebayes_bundle", model.transform(data))