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
