def test_numpy_deserializer_from_npy_object_array_with_allow_pickle_false():
numpy_deserializer = NumpyDeserializer(allow_pickle=False)
array = np.array([{"a": "", "b": ""}, {"c": "", "d": ""}])
stream = io.BytesIO()
np.save(stream, array)
stream.seek(0)
with pytest.raises(ValueError):
numpy_deserializer.deserialize(stream, "application/x-npy")
def __init__(
self,
endpoint_name,
sagemaker_session=None,
serializer=NumpySerializer(),
deserializer=NumpyDeserializer(),
):
"""Initialize an ``SKLearnPredictor``.
Args:
endpoint_name (str): The name of the endpoint to perform inference
on.
sagemaker_session (sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, the estimator creates one
using the default AWS configuration chain.
serializer (sagemaker.serializers.BaseSerializer): Optional. Default
serializes input data to .npy format. Handles lists and numpy
arrays.
deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
Default parses the response from .npy format to numpy array.
"""
super(SKLearnPredictor, self).__init__(
endpoint_name,
sagemaker_session,
serializer=serializer,
deserializer=deserializer,
)
def __init__(self, endpoint_name, sagemaker_session=None):
"""Initialize an ``PyTorchPredictor``.
Args:
endpoint_name (str): The name of the endpoint to perform inference
on.
sagemaker_session (sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, the estimator creates one
using the default AWS configuration chain.
"""
super(PyTorchPredictor, self).__init__(
endpoint_name, sagemaker_session, NumpySerializer(), NumpyDeserializer()
)
production_variants = endpoint_config["ProductionVariants"]
return [d["ModelName"] for d in production_variants]
@property
def content_type(self):
"""The MIME type of the data sent to the inference endpoint."""
return self.serializer.CONTENT_TYPE
@property
def accept(self):
"""The content type(s) that are expected from the inference endpoint."""
return self.deserializer.ACCEPT
@property
def endpoint(self):
"""Deprecated attribute. Please use endpoint_name."""
renamed_warning("The endpoint attribute")
return self.endpoint_name
csv_serializer = deprecated_serialize(CSVSerializer(), "csv_serializer")
json_serializer = deprecated_serialize(JSONSerializer(), "json_serializer")
npy_serializer = deprecated_serialize(NumpySerializer(), "npy_serializer")
csv_deserializer = deprecated_deserialize(CSVDeserializer(),
"csv_deserializer")
json_deserializer = deprecated_deserialize(JSONDeserializer(),
"json_deserializer")
numpy_deserializer = deprecated_deserialize(NumpyDeserializer(),
"numpy_deserializer")
RealTimePredictor = deprecated_class(Predictor, "RealTimePredictor")
def numpy_deserializer():
return NumpyDeserializer()
def test_numpy_deserializer_from_json_alpha():
numpy_deserializer = NumpyDeserializer(dtype="U5")
stream = io.BytesIO(b'[["hello",2,3],\n[4,5,6]]')
array = numpy_deserializer.deserialize(stream, "application/json")
assert np.array_equal(array, np.array([["hello", 2, 3], [4, 5, 6]]))
def test_numpy_deserializer_from_csv_alpha():
numpy_deserializer = NumpyDeserializer(dtype="U5")
stream = io.BytesIO(b"hello,2,3\n4,5,6")
array = numpy_deserializer.deserialize(stream, "text/csv")
assert np.array_equal(array, np.array([["hello", 2, 3], [4, 5, 6]]))
def attach_predictor(self):
self.predictor = SagemakerPredictor(endpoint_name=self.endpoint_name,
sagemaker_session=self.session,
serializer=NumpySerializer(),
deserializer=NumpyDeserializer())
