def test_retrying_rpc_exception():
first_call = [True]
def predict_mock(request, timeout):
inputs = tf.contrib.util.make_ndarray(request.inputs['images'])
assert all([x == y for x, y in zip(inputs[0], [1, 2])])
if (first_call[0]):
first_call[0] = False
raise grpc.RpcError()
return_data = np.asarray([[11, 22]])
return_tensor = tf.contrib.util.make_tensor_proto(
return_data, types_pb2.DT_FLOAT, return_data.shape)
result = mock.MagicMock()
result.outputs = {"output_alias": return_tensor}
return result
stub_mock = mock.Mock()
stub_mock.Predict = mock.MagicMock(side_effect=predict_mock)
client = PredictionClient("localhost", 50051)
client._get_grpc_stub = lambda: stub_mock
result = client.score_numpy_array(np.asarray([[1, 2]], dtype='f'))
assert all([x == y for x, y in zip(result[0], [11, 22])])
class RemoteQuantizedResNet50(QuantizedResNet50):
def __init__(self,
subscription_id,
resource_group,
model_management_account,
model_base_path,
remote_service_name=None):
super().__init__(model_base_path)
self.__deployment_client = DeploymentClient(subscription_id,
resource_group,
model_management_account)
self.__service_name = remote_service_name if remote_service_name is not None else "featurizer-service-" + hashlib.md5(
(self.model_name + "-" +
self.version).encode("utf-8")).hexdigest()[:6]
def _import_featurizer_graph_def(self, input_map):
service = self.__deployment_client.get_service_by_name(
self.__service_name)
if (service is None):
model_name = self.model_name + "-" + self.version + "-model"
temp_dir = tempfile.mkdtemp()
model_path = os.path.join(temp_dir, "model")
service_def = ServiceDefinition()
service_def.pipeline.append(BrainWaveStage(self))
service_def.save(model_path)
model_id = self.__deployment_client.register_model(
model_name, model_path)
service = self.__deployment_client.create_service(
self.__service_name, model_id)
self.__client = PredictionClient(service.ipAddress, service.port) #pylint: disable=E1101
return input_map['InputImage'], tf.py_func(self._remote_service_call,
[input_map['InputImage']],
tf.float32)
def _remote_service_call(self, data):
return self.__client.score_numpy_array(data)
def cleanup_remote_service(self):
service = self.__deployment_client.get_service_by_name(
self.__service_name)
if service is not None:
print("Deleting service", service.id)
self.__deployment_client.delete_service(service.id)
print("Deleted service", service.id)
print("Deleting model", service.modelId)
self.__deployment_client.delete_model(service.modelId)
print("Deleted model", service.modelId)
def test_retrying_rpc_exception():
first_call = [True]
channel_mock_loaded = {'value': 0}
channel_mock_closed = {'value': 0}
def unary_unary(id, request_serializer, response_deserializer):
result = mock.MagicMock()
if id == '/tensorflow.serving.PredictionService/Predict':
if (first_call[0]):
first_call[0] = False
return lambda req, timeout: (_ for _ in
()).throw(grpc.RpcError())
return_data = np.asarray([[11, 22]])
return_tensor = tf.contrib.util.make_tensor_proto(
return_data, types_pb2.DT_FLOAT, return_data.shape)
result.outputs = {"output_alias": return_tensor}
return lambda req, timeout: result
def load_channel_mock():
channel_mock_loaded['value'] += 1
return channel_mock
def close_channel_mock():
channel_mock_closed['value'] += 1
now = datetime.now()
channel_mock = mock.Mock()
channel_mock.unary_unary = mock.MagicMock(side_effect=unary_unary)
channel_mock.close = close_channel_mock
client = PredictionClient("localhost",
50051,
channel_shutdown_timeout=timedelta(minutes=1))
client._channel_func = load_channel_mock
client._get_datetime_now = lambda: now
result = client.score_numpy_array(np.asarray([[1, 2]], dtype='f'))
assert all([x == y for x, y in zip(result[0], [11, 22])])
assert channel_mock_loaded['value'] == 2
assert channel_mock_closed['value'] == 1
def test_score_numpy_array():
def predict_mock(request, timeout):
inputs = tf.contrib.util.make_ndarray(request.inputs['images'])
assert all([x == y for x, y in zip(inputs[0], [ 1, 2, 3 ])])
assert all([x == y for x, y in zip(inputs[1], [ 4, 5, 6 ])])
return_data = np.asarray([[ 11, 22, 33 ], [ 44, 55, 66 ]])
return_tensor = tf.contrib.util.make_tensor_proto(return_data, types_pb2.DT_FLOAT, return_data.shape)
result = mock.MagicMock()
result.outputs = { "output_alias": return_tensor }
return result
stub_mock = mock.Mock()
stub_mock.Predict = mock.MagicMock(side_effect=predict_mock)
client = PredictionClient("localhost", 50051)
client.stub = stub_mock
result = client.score_numpy_array(np.asarray([[1, 2, 3], [4, 5, 6]], dtype='f'))
assert all([x == y for x, y in zip(result[0], [ 11, 22, 33 ])])
assert all([x == y for x, y in zip(result[1], [ 44, 55, 66 ])])