def __init__(
self,
trainingInstanceType,
trainingInstanceCount,
endpointInstanceType,
endpointInitialInstanceCount,
sagemakerRole=IAMRoleFromConfig(),
requestRowSerializer=ProtobufRequestRowSerializer(),
responseRowDeserializer=LinearLearnerBinaryClassifierProtobufResponseRowDeserializer(),
trainingInputS3DataPath=S3AutoCreatePath(),
trainingOutputS3DataPath=S3AutoCreatePath(),
trainingInstanceVolumeSizeInGB=1024,
trainingProjectedColumns=None,
trainingChannelName="train",
trainingContentType=None,
trainingS3DataDistribution="ShardedByS3Key",
trainingSparkDataFormat="sagemaker",
trainingSparkDataFormatOptions=None,
trainingInputMode="File",
trainingCompressionCodec=None,
trainingMaxRuntimeInSeconds=24*60*60,
trainingKmsKeyId=None,
modelEnvironmentVariables=None,
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_CONSTRUCT,
sagemakerClient=SageMakerClients.create_sagemaker_client(),
region=None,
s3Client=SageMakerClients.create_s3_default_client(),
stsClient=SageMakerClients.create_sts_default_client(),
modelPrependInputRowsToTransformationRows=True,
deleteStagingDataAfterTraining=True,
namePolicyFactory=RandomNamePolicyFactory(),
uid=None,
javaObject=None):
if trainingSparkDataFormatOptions is None:
trainingSparkDataFormatOptions = {}
if modelEnvironmentVariables is None:
modelEnvironmentVariables = {}
if uid is None:
uid = Identifiable._randomUID()
kwargs = locals().copy()
del kwargs['self']
super(LinearLearnerBinaryClassifier, self).__init__(**kwargs)
default_params = {
'predictor_type': 'binary_classifier'
}
self._setDefault(**default_params)
def __init__(self,
endpointInstanceType,
endpointInitialInstanceCount,
requestRowSerializer,
responseRowDeserializer,
existingEndpointName=None,
modelImage=None,
modelPath=None,
modelEnvironmentVariables=None,
modelExecutionRoleARN=None,
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_CONSTRUCT,
sagemakerClient=SageMakerClients.create_sagemaker_client(),
prependResultRows=True,
namePolicy=RandomNamePolicy(),
uid=None,
javaObject=None):
super(SageMakerModel, self).__init__()
if modelEnvironmentVariables is None:
modelEnvironmentVariables = {}
if javaObject:
self._java_obj = javaObject
else:
if uid is None:
uid = Identifiable._randomUID()
self._java_obj = self._new_java_obj(
SageMakerModel._wrapped_class,
Option(endpointInstanceType),
Option(endpointInitialInstanceCount),
requestRowSerializer,
responseRowDeserializer,
Option(existingEndpointName),
Option(modelImage),
Option(modelPath),
modelEnvironmentVariables,
Option(modelExecutionRoleARN),
endpointCreationPolicy,
sagemakerClient,
prependResultRows,
namePolicy,
uid
)
self._resetUid(self._call_java("uid"))
def test_spark_integration():
key = SparkMLParam(Identifiable(), "name", "doc")
value = 123
param = Param(key, value)
assert param.key == "name"
assert param.value == "123"