def __get_tf_server_connection_params__(): ''' Returns connection parameters to TensorFlow Server :return: Tuple of TF server name and server port ''' server_name = utils.get_env_var_setting('TF_SERVER_NAME', settings.DEFAULT_TF_SERVER_NAME) server_port = utils.get_env_var_setting('TF_SERVER_PORT', settings.DEFAULT_TF_SERVER_PORT) return server_name, server_port
def __get_tf_server_connection_params__(): ''' Returns connection parameters to TensorFlow Server :return: Tuple of TF server name and server port ''' server_name = utils.get_env_var_setting('TF_SERVER_NAME', settings.DEFAULT_TF_SERVER_NAME) server_port = utils.get_env_var_setting('TF_SERVER_PORT', settings.DEFAULT_TF_SERVER_PORT) return server_name, server_port
def __get_flask_server_params__(): ''' Returns connection parameters of the Flask application :return: Tripple of server name, server port and debug settings ''' server_name = utils.get_env_var_setting('FLASK_SERVER_NAME', settings.DEFAULT_FLASK_SERVER_NAME) server_port = utils.get_env_var_setting('FLASK_SERVER_PORT', settings.DEFAULT_FLASK_SERVER_PORT) flask_debug = utils.get_env_var_setting('FLASK_DEBUG', settings.DEFAULT_FLASK_DEBUG) flask_debug = True if flask_debug == '1' else False return server_name, server_port, flask_debug
def __get_flask_server_params__(): ''' Returns connection parameters of the Flask application :return: Tripple of server name, server port and debug settings ''' server_name = utils.get_env_var_setting('FLASK_SERVER_NAME', settings.DEFAULT_FLASK_SERVER_NAME) server_port = int(utils.get_env_var_setting('FLASK_SERVER_PORT', settings.DEFAULT_FLASK_SERVER_PORT)) flask_debug = utils.get_env_var_setting('FLASK_DEBUG', settings.DEFAULT_FLASK_DEBUG) flask_debug = True if flask_debug == '1' else False return server_name, server_port, flask_debug
def __setup_tokenizer(): # ende/1539080952/assets.extra/wmtende.model # tokenizer = pyonmttok.Tokenizer("none", sp_model_path=args.sentencepiece_model) SP_MODEL = utils.get_env_var_setting( 'ENDE_MODEL_SENTENCE_PIECE', settings.DEFAULT_ENDE_MODEL_SENTENCE_PIECE) log.debug('setup tokenizer:' + SP_MODEL) tokenizer = pyonmttok.Tokenizer("none", sp_model_path=SP_MODEL) return tokenizer
def __create_prediction_request__(tokenizer, input_data): ''' Creates prediction request to TensorFlow server for ENDE model :param: Byte array, input_data for prediction :return: PredictRequest object ''' # create predict request request = predict_pb2.PredictRequest() log.debug( 'create prediction request: tokenize, length and tokens MADNESS') # Tensorflow magic MODEL_NAME = utils.get_env_var_setting('ENDE_MODEL_NAME', settings.DEFAULT_ENDE_MODEL_NAME) log.debug('using model:' + MODEL_NAME) request.model_spec.name = MODEL_NAME # TODO: using signature_def as signature_name - is that correct? IDK # hint: python yada/lib/python3.5/site-packages/tensorflow/python/tools/saved_model_cli.py show --dir yada/ende/1539080952/ --all SIGNATURE_NAME = utils.get_env_var_setting( 'ENDE_MODEL_SIGNATURE_NAME', settings.DEFAULT_ENDE_MODEL_SIGNATURE_NAME) log.debug('using signature:' + SIGNATURE_NAME) request.model_spec.signature_name = SIGNATURE_NAME log.debug('building tokens') input_tokens = [tokenizer.tokenize(text)[0] for text in input_data] log.debug(type(input_tokens)) log.debug(input_tokens) batch_tokens, lengths, max_length = pad_batch(input_tokens) batch_size = len(lengths) request.inputs['tokens'].CopyFrom( tf.contrib.util.make_tensor_proto(batch_tokens, shape=(batch_size, max_length))) log.debug('building length') request.inputs['length'].CopyFrom( tf.contrib.util.make_tensor_proto(lengths, shape=(batch_size, ))) log.debug('throw request to the grpc - here is the request:') log.debug(request) return request