def testRegress(self):
"""Test PredictionService.Regress implementation."""
model_path = self._GetSavedModelBundlePath()
atexit.register(self.TerminateProcs)
model_server_address = self.RunServer(PickUnusedPort(), 'default',
model_path)
print 'Sending Regress request...'
# Prepare request
request = regression_pb2.RegressionRequest()
request.model_spec.name = 'default'
request.model_spec.signature_name = 'regress_x_to_y'
example = request.input.example_list.examples.add()
example.features.feature['x'].float_list.value.extend([2.0])
# Send request
channel = grpc.insecure_channel(model_server_address)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
result = stub.Regress(request, RPC_TIMEOUT) # 5 secs timeout
# Verify response
self.assertEquals(1, len(result.result.regressions))
expected_output = 3.0
self.assertEquals(expected_output, result.result.regressions[0].value)
self._VerifyModelSpec(result.model_spec, request.model_spec.name,
request.model_spec.signature_name,
self._GetModelVersion(model_path))
def testRegress(self):
"""Test PredictionService.Regress implementation."""
model_path = self._GetSavedModelBundlePath()
use_saved_model = True
atexit.register(self.TerminateProcs)
model_server_address = self.RunServer(PickUnusedPort(), 'default',
model_path, use_saved_model)
time.sleep(5)
print 'Sending Regress request...'
# Prepare request
request = regression_pb2.RegressionRequest()
request.model_spec.name = 'default'
request.model_spec.signature_name = 'regress_x_to_y'
example = request.input.example_list.examples.add()
example.features.feature['x'].float_list.value.extend([2.0])
# Send request
host, port = model_server_address.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(
channel)
result = stub.Regress(request, 5.0) # 5 secs timeout
# Verify response
self.assertEquals(1, len(result.result.regressions))
expected_output = 3.0
self.assertEquals(expected_output, result.result.regressions[0].value)
def _TestRegress(self, model_path):
"""Test PredictionService.Regress implementation."""
model_server_address = TensorflowModelServerTest.RunServer(
'default', model_path)[1]
print('Sending Regress request...')
# Prepare request
request = regression_pb2.RegressionRequest()
request.model_spec.name = 'default'
request.model_spec.signature_name = 'regress_x_to_y'
example = request.input.example_list.examples.add()
example.features.feature['x'].float_list.value.extend([2.0])
# Send request
channel = grpc.insecure_channel(model_server_address)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
result = stub.Regress(request, RPC_TIMEOUT) # 5 secs timeout
# Verify response
self.assertEqual(1, len(result.result.regressions))
expected_output = 3.0
self.assertEqual(expected_output, result.result.regressions[0].value)
self._VerifyModelSpec(result.model_spec, request.model_spec.name,
request.model_spec.signature_name,
self._GetModelVersion(model_path))
def _BuildRegressionRequests(self, signature_name: str):
for example in self.examples:
request = regression_pb2.RegressionRequest()
request.model_spec.name = self._model_name
request.model_spec.signature_name = signature_name
request.input.example_list.examples.append(example)
yield request
def call_servo(examples, serving_bundle):
"""Send an RPC request to the Servomatic prediction service.
Args:
examples: A list of tf.train.Examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the serving request.
Returns:
A ClassificationResponse or RegressionResponse proto.
"""
parsed_url = urlparse('http://' + serving_bundle.inference_address)
channel = implementations.insecure_channel(parsed_url.hostname,
parsed_url.port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
if serving_bundle.model_type == 'classification':
request = classification_pb2.ClassificationRequest()
else:
request = regression_pb2.RegressionRequest()
request.model_spec.name = serving_bundle.model_name
if serving_bundle.model_version is not None:
request.model_spec.version.value = serving_bundle.model_version
if serving_bundle.signature is not None:
request.model_spec.signature_name = serving_bundle.signature
request.input.example_list.examples.extend(examples)
if serving_bundle.model_type == 'classification':
return stub.Classify(request, 30.0) # 30 secs timeout
else:
return stub.Regress(request, 30.0) # 30 secs timeout
def parse_request(self, serialized_data):
request_fn_map = {
PREDICT: lambda: predict_pb2.PredictRequest(),
INFERENCE: lambda: inference_pb2.MultiInferenceRequest(),
CLASSIFY: lambda: classification_pb2.ClassificationRequest(),
REGRESSION: lambda: regression_pb2.RegressionRequest()
}
request = request_fn_map[self.prediction_type]()
request.ParseFromString(serialized_data)
return request
def testIssueRequests_NoErrorIfSucceeded(self):
# Prepare requests and client.
r1 = classification_pb2.ClassificationRequest()
r2 = classification_pb2.ClassificationRequest()
r3 = regression_pb2.RegressionRequest()
client = TensorFlowServingClient('localhost:1234', 'a_model_name')
# Call.
client.IssueRequests([r1, r2, r3])
# Check calls
self.prediction_stub.Classify.assert_called_with(r1)
self.prediction_stub.Classify.assert_called_with(r2)
self.prediction_stub.Regress.assert_called_with(r3)
def _ExampleToRegressionRequest(
self,
example: tf.train.Example) -> regression_pb2.RegressionRequest:
"""Convert single Example to RegressionRequest.
Args:
example: `Example` instance to convert.
Returns:
A converted `RegressionRequest` instance.
"""
request = regression_pb2.RegressionRequest()
request.model_spec.name = self._model_name
request.model_spec.signature_name = self._signature_name
request.input.example_list.examples.append(example)
return request
def call_servo(examples, serving_bundle):
"""Send an RPC request to the Servomatic prediction service.
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the serving request.
Returns:
A ClassificationResponse or RegressionResponse proto.
"""
parsed_url = urlparse('http://' + serving_bundle.inference_address)
channel = implementations.insecure_channel(parsed_url.hostname,
parsed_url.port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
if serving_bundle.use_predict:
request = predict_pb2.PredictRequest()
elif serving_bundle.model_type == 'classification':
request = classification_pb2.ClassificationRequest()
else:
request = regression_pb2.RegressionRequest()
request.model_spec.name = serving_bundle.model_name
if serving_bundle.model_version is not None:
request.model_spec.version.value = serving_bundle.model_version
if serving_bundle.signature is not None:
request.model_spec.signature_name = serving_bundle.signature
if serving_bundle.use_predict:
# tf.compat.v1 API used here to convert tf.example into proto. This
# utility file is bundled in the witwidget pip package which has a dep
# on TensorFlow.
request.inputs[serving_bundle.predict_input_tensor].CopyFrom(
tf.compat.v1.make_tensor_proto(
values=[ex.SerializeToString() for ex in examples],
dtype=types_pb2.DT_STRING))
else:
request.input.example_list.examples.extend(examples)
if serving_bundle.use_predict:
return common_utils.convert_predict_response(
stub.Predict(request, 30.0), serving_bundle) # 30 secs timeout
elif serving_bundle.model_type == 'classification':
return stub.Classify(request, 30.0) # 30 secs timeout
else:
return stub.Regress(request, 30.0) # 30 secs timeout
def main(_):
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = regression_pb2.RegressionRequest()
request.model_spec.name = 'lr_1'
request.model_spec.version.value = 1503585217
example = request.input.example_list.examples.add()
example.features.feature['AGE'].float_list.value.extend([1.2])
example.features.feature['B'].float_list.value.extend([1.2])
example.features.feature['CHAS'].float_list.value.extend([1.2])
example.features.feature['CRIM'].float_list.value.extend([1.2])
example.features.feature['DIS'].float_list.value.extend([1.2])
result = stub.Regress(request, 10.0) # 10 secs timeout
print(result)
print(' # Preparing data ')
features_data=[34.49726772511229, 12.65565114916675, 39.57766801952616, 4.0826206329529615]
model_input = tf.train.Example(
features=tf.train.Features(
feature={
'inputs': tf.train.Feature(
float_list=tf.train.FloatList(value=features_data)
)
}
)
)
print(' # Preparing predictor ')
endpoint='SageMakerEndpoint'
predictor=RealTimePredictor(
endpoint=endpoint,
deserializer=tf_deserializer,
serializer=tf_serializer,
content_type='application/octet-stream')
print(' # Preparing request ')
request=regression_pb2.RegressionRequest()
request.model_spec.name='generic_model'
request.model_spec.signature_name=DEFAULT_SERVING_SIGNATURE_DEF_KEY
request.input.example_list.examples.extend([model_input])
print(' # Invoking SageMaker API ')
result=predictor.predict(request)
print(result)
def call_servo(examples, serving_bundle):
"""Send an RPC request to the Servomatic prediction service.
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the serving request.
Returns:
A ClassificationResponse or RegressionResponse proto.
"""
parsed_url = urlparse('http://' + serving_bundle.inference_address)
channel = implementations.insecure_channel(parsed_url.hostname,
parsed_url.port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
if serving_bundle.use_predict:
request = predict_pb2.PredictRequest()
elif serving_bundle.model_type == 'classification':
request = classification_pb2.ClassificationRequest()
else:
request = regression_pb2.RegressionRequest()
request.model_spec.name = serving_bundle.model_name
if serving_bundle.model_version is not None:
request.model_spec.version.value = serving_bundle.model_version
if serving_bundle.signature is not None:
request.model_spec.signature_name = serving_bundle.signature
if serving_bundle.use_predict:
# tf.compat.v1 API used here to convert tf.example into proto. This
# utility file is bundled in the witwidget pip package which has a dep
# on TensorFlow.
predict_input_tensor_list = serving_bundle.predict_input_tensor.split(
",")
if len(examples) > 0:
for key in predict_input_tensor_list:
dtype = types_pb2.DT_STRING
for value in examples[0].features.feature.items():
if key == str(value[0]):
if "int64" in str(value[1]):
dtype = types_pb2.DT_INT64
elif "float" in str(value[1]):
dtype = types_pb2.DT_FLOAT
break
values = []
for ex in examples:
if dtype == types_pb2.DT_FLOAT:
values += ex.features.feature[key].float_list.value
elif dtype == types_pb2.DT_INT64:
values += ex.features.feature[key].int64_list.value
request.inputs[key].CopyFrom(
tf.compat.v1.make_tensor_proto(values=values, dtype=dtype))
else:
print("examples length is %d" % len(examples))
else:
request.input.example_list.examples.extend(examples)
if serving_bundle.use_predict:
return common_utils.convert_predict_response(
stub.Predict(request, 30.0), serving_bundle) # 30 secs timeout
elif serving_bundle.model_type == 'classification':
return stub.Classify(request, 30.0) # 30 secs timeout
else:
return stub.Regress(request, 30.0) # 30 secs timeout
