def _grpc(self, arr: np.array) -> np.array:
options = [('grpc.max_send_message_length', GRPC_MAX_MSG_LEN),
('grpc.max_receive_message_length', GRPC_MAX_MSG_LEN)]
channel = grpc.insecure_channel(self.predictor_host, options)
stub = prediction_pb2_grpc.SeldonStub(channel)
if self.tf_data_type is not None:
datadef = prediction_pb2.DefaultData(
tftensor=tf.make_tensor_proto(arr, self.tf_data_type))
else:
datadef = prediction_pb2.DefaultData(
tftensor=tf.make_tensor_proto(arr))
request = prediction_pb2.SeldonMessage(data=datadef)
response = stub.Predict(request=request)
arr_resp = tf.make_ndarray(response.data.tftensor)
return arr_resp
def _grpc(arr: np.array, predictor_host: str,
tf_data_type: Optional[str]) -> np.array:
options = [
("grpc.max_send_message_length", GRPC_MAX_MSG_LEN),
("grpc.max_receive_message_length", GRPC_MAX_MSG_LEN),
# TODO: Test skip functionality with gRPC's metadata
]
channel = grpc.insecure_channel(predictor_host, options)
stub = prediction_pb2_grpc.SeldonStub(channel)
if tf_data_type is not None:
datadef = prediction_pb2.DefaultData(
tftensor=tf.make_tensor_proto(arr, tf_data_type))
else:
datadef = prediction_pb2.DefaultData(
tftensor=tf.make_tensor_proto(arr))
request = prediction_pb2.SeldonMessage(data=datadef)
request_metadata = ((SELDON_SKIP_LOGGING_HEADER, "true"), )
response = stub.Predict(request=request, metadata=request_metadata)
arr_resp = tf.make_ndarray(response.data.tftensor)
return arr_resp