def multi_inference(body):
prediction_service_stub = _get_prediction_service_stub()
request = ParseDict(body, inference.MultiInferenceRequest())
result = prediction_service_stub.MultiInference(request)
return MessageToDict(result,
preserving_proto_field_name=True,
including_default_value_fields=True)
def testMultiInference(self):
"""Test PredictionService.MultiInference implementation."""
model_path = self._GetSavedModelBundlePath()
model_server_address = TensorflowModelServerTest.RunServer(
'default', model_path)[1]
print 'Sending MultiInference request...'
# Prepare request
request = inference_pb2.MultiInferenceRequest()
request.tasks.add().model_spec.name = 'default'
request.tasks[0].model_spec.signature_name = 'regress_x_to_y'
request.tasks[0].method_name = 'tensorflow/serving/regress'
request.tasks.add().model_spec.name = 'default'
request.tasks[1].model_spec.signature_name = 'classify_x_to_y'
request.tasks[1].method_name = 'tensorflow/serving/classify'
example = request.input.example_list.examples.add()
example.features.feature['x'].float_list.value.extend([2.0])
# Send request
result = self._MakeStub(model_server_address).MultiInference(
request, RPC_TIMEOUT)
# Verify response
self.assertEquals(2, len(result.results))
expected_output = 3.0
self.assertEquals(expected_output,
result.results[0].regression_result.regressions[0].value)
self.assertEquals(expected_output, result.results[
1].classification_result.classifications[0].classes[0].score)
for i in xrange(2):
self._VerifyModelSpec(result.results[i].model_spec,
request.tasks[i].model_spec.name,
request.tasks[i].model_spec.signature_name,
self._GetModelVersion(model_path))
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 testMultiInference(self):
"""Test PredictionService.MultiInference implementation."""
model_path = self._GetSavedModelBundlePath()
enable_batching = False
atexit.register(self.TerminateProcs)
model_server_address = self.RunServer(PickUnusedPort(), 'default',
model_path, enable_batching)
print 'Sending MultiInference request...'
# Prepare request
request = inference_pb2.MultiInferenceRequest()
request.tasks.add().model_spec.name = 'default'
request.tasks[0].model_spec.signature_name = 'regress_x_to_y'
request.tasks[0].method_name = 'tensorflow/serving/regress'
request.tasks.add().model_spec.name = 'default'
request.tasks[1].model_spec.signature_name = 'classify_x_to_y'
request.tasks[1].method_name = 'tensorflow/serving/classify'
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.MultiInference(request, RPC_TIMEOUT) # 5 secs timeout
# Verify response
self.assertEquals(2, len(result.results))
expected_output = 3.0
self.assertEquals(
expected_output,
result.results[0].regression_result.regressions[0].value)
self.assertEquals(
expected_output, result.results[1].classification_result.
classifications[0].classes[0].score)
for i in xrange(2):
self._VerifyModelSpec(result.results[i].model_spec,
request.tasks[i].model_spec.name,
request.tasks[i].model_spec.signature_name,
self._GetModelVersion(model_path))
def _TestMultiInference(self, model_path):
"""Test PredictionService.MultiInference implementation."""
model_server_address = TensorflowModelServerTest.RunServer(
'default', model_path)[1]
print('Sending MultiInference request...')
# Prepare request
request = inference_pb2.MultiInferenceRequest()
request.tasks.add().model_spec.name = 'default'
request.tasks[0].model_spec.signature_name = 'regress_x_to_y'
request.tasks[0].method_name = 'tensorflow/serving/regress'
request.tasks.add().model_spec.name = 'default'
request.tasks[1].model_spec.signature_name = 'classify_x_to_y'
request.tasks[1].method_name = 'tensorflow/serving/classify'
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.MultiInference(request, RPC_TIMEOUT) # 5 secs timeout
# Verify response
self.assertEqual(2, len(result.results))
expected_output = 3.0
self.assertEqual(
expected_output,
result.results[0].regression_result.regressions[0].value)
self.assertEqual(
expected_output, result.results[1].classification_result.
classifications[0].classes[0].score)
for i in range(2):
self._VerifyModelSpec(result.results[i].model_spec,
request.tasks[i].model_spec.name,
request.tasks[i].model_spec.signature_name,
self._GetModelVersion(model_path))
