in sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py [0:0]
def _get_java_obj(self, **kwargs):
return self._new_java_obj(
SageMakerEstimator._wrapped_class,
kwargs['trainingImage'],
kwargs['modelImage'],
kwargs['sagemakerRole'],
kwargs['trainingInstanceType'],
kwargs['trainingInstanceCount'],
kwargs['endpointInstanceType'],
kwargs['endpointInitialInstanceCount'],
kwargs['requestRowSerializer'],
kwargs['responseRowDeserializer'],
kwargs['trainingInputS3DataPath'],
kwargs['trainingOutputS3DataPath'],
kwargs['trainingInstanceVolumeSizeInGB'],
Option(kwargs['trainingProjectedColumns']),
kwargs['trainingChannelName'],
Option(kwargs['trainingContentType']),
kwargs['trainingS3DataDistribution'],
kwargs['trainingSparkDataFormat'],
kwargs['trainingSparkDataFormatOptions'],
kwargs['trainingInputMode'],
Option(kwargs['trainingCompressionCodec']),
kwargs['trainingMaxRuntimeInSeconds'],
Option(kwargs['trainingKmsKeyId']),
kwargs['modelEnvironmentVariables'],
kwargs['endpointCreationPolicy'],
kwargs['sagemakerClient'],
kwargs['s3Client'],
kwargs['stsClient'],
kwargs['modelPrependInputRowsToTransformationRows'],
kwargs['deleteStagingDataAfterTraining'],
kwargs['namePolicyFactory'],
kwargs['uid'],
kwargs['hyperParameters']
)