Py4JJavaError: An error occurred while calling o391.load.

Hello
What am I doing wrong here?
When I run the second cell in the "pyspark_mnist_custom_estimator" notebook to load the data, I get the following error:
Py4JJavaError Traceback (most recent call last)
in ()
4 # spark._jsc.hadoopConfiguration().set('fs.s3a.endpoint', 's3.{}.amazonaws.com'.format(region))
5
----> 6 trainingData = spark.read.format('libsvm') .option('numFeatures', '784') .load('s3a://sagemaker-sample-data-{}/spark/mnist/train/'.format(region))
7
8 #testData = spark.read.format('libsvm')\

~/anaconda3/envs/python3/lib/python3.6/site-packages/pyspark/sql/readwriter.py in load(self, path, format, schema, **options)
164 self.options(**options)
165 if isinstance(path, basestring):
--> 166 return self._df(self._jreader.load(path))
167 elif path is not None:
168 if type(path) != list:

~/anaconda3/envs/python3/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:

~/anaconda3/envs/python3/lib/python3.6/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()

~/anaconda3/envs/python3/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(

Py4JJavaError: An error occurred while calling o391.load.
: java.util.ServiceConfigurationError: org.apache.spark.sql.sources.DataSourceRegister: Provider com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufFileFormat could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:44)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
at scala.collection.TraversableLike.filterImpl(TraversableLike.scala:250)
at scala.collection.TraversableLike.filterImpl$(TraversableLike.scala:248)
at scala.collection.AbstractTraversable.filterImpl(Traversable.scala:108)
at scala.collection.TraversableLike.filter(TraversableLike.scala:262)
at scala.collection.TraversableLike.filter$(TraversableLike.scala:262)
at scala.collection.AbstractTraversable.filter(Traversable.scala:108)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:630)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:194)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NoClassDefFoundError: org/apache/spark/sql/execution/datasources/FileFormat$class
at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufFileFormat.(SageMakerProtobufFileFormat.scala:41)

Start with the second cell of the "pyspark_mnist_custom_estimator" notebook and reproduce the failure while loading the S3A MNIST path. Trace the SageMakerProtobufFileFormat initialization and its Spark dependency compatibility; done means the training data loads without the reported Py4JJavaError.

Written by the indexing model from the issue text.