tensorflow monitoredsession 사용법

Question

간단한 산술 계산을 수행하려면 다음 코드가 필요합니다. 나는 모니터링 된 교육 세션을 사용하여 내결함성을 구현하려고합니다.

import tensorflow as tf 

global_step_tensor = tf.Variable(10, trainable=False, name='global_step') 

cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223","localhost:2224", "localhost:2225"]}) 
x = tf.constant(2) 

with tf.device("/job:local/task:0"): 
    y1 = x + 300 

with tf.device("/job:local/task:1"): 
    y2 = x**2 

with tf.device("/job:local/task:2"): 
    y3 = 5*x 

with tf.device("/job:local/task:3"): 
    y0 = x - 66 
    y = y0 + y1 + y2 + y3 

ChiefSessionCreator = tf.train.ChiefSessionCreator(scaffold=None, master='localhost:2222', config='grpc://localhost:2222', checkpoint_dir='/home/tensorflow/codes/checkpoints') 
saver_hook = tf.train.CheckpointSaverHook(checkpoint_dir='/home/tensorflow/codes/checkpoints', save_secs=10, save_steps=None, saver=y, checkpoint_basename='model.ckpt', scaffold=None) 
summary_hook = tf.train.SummarySaverHook(save_steps=None, save_secs=10, output_dir='/home/tensorflow/codes/savepoints', summary_writer=None, scaffold=None, summary_op=y) 

with tf.train.MonitoredTrainingSession(master='localhost:2222', is_chief=True, checkpoint_dir='/home/tensorflow/codes/checkpoints', 
    scaffold=None, hooks=[saver_hook, summary_hook], chief_only_hooks=None, save_checkpoint_secs=10, save_summaries_steps=None, config='grpc://localhost:2222') as sess: 

    while not sess.should_stop(): 
     sess.run(model) 

    while not sess.should_stop(): 
     print(sess.run(y0)) 
     print('\n') 

    while not sess.should_stop(): 
     print(sess.run(y1)) 
     print('\n') 

    while not sess.should_stop(): 
     print(sess.run(y2)) 
     print('\n') 

    while not sess.should_stop(): 
     print(sess.run(y3)) 
     print('\n') 

    while not sess.should_stop(): 
     result = sess.run(y) 
     print(result) 

그러나 다음과 같은 오류를 던지고있다 : - 내 의견으로는 설정에 주어진 잘못된 인수로 인해

Traceback (most recent call last): 
    File "add_1.py", line 36, in <module> 
    scaffold=None, hooks=[saver_hook, summary_hook], chief_only_hooks=None, save_checkpoint_secs=10, save_summaries_steps=None, config='grpc://localhost:2222') as sess: 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 289, in MonitoredTrainingSession 
    return MonitoredSession(session_creator=session_creator, hooks=hooks) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 447, in __init__ 
    self._sess = _RecoverableSession(self._coordinated_creator) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 618, in __init__ 
    _WrappedSession.__init__(self, self._sess_creator.create_session()) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 505, in create_session 
    self.tf_sess = self._session_creator.create_session() 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 341, in create_session 
    init_fn=self._scaffold.init_fn) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 227, in prepare_session 
    config=config) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 153, in _restore_checkpoint 
    sess = session.Session(self._target, graph=self._graph, config=config) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1186, in __init__ 
    super(Session, self).__init__(target, graph, config=config) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 540, in __init__ 
    % type(config)) 
TypeError: config must be a tf.ConfigProto, but got <type 'str'> 
Exception AttributeError: "'Session' object has no attribute '_session'" in <bound method Session.__del__ of <tensorflow.python.client.session.Session object at 0x7fb1bac14ed0>> ignored 

. 매개 변수를 올바르게 사용하고 있습니까? 제발 조언.

Answer 1

첫 번째 문제는 다음 줄에 나와 있습니다. 분산 (장치 할당) 작업을 위해 로컬 세션을 사용합니다. 왜 그게 필요하니?

sess = tf.Session() 
tf.train.global_step(sess, global_step_tensor) 

두 번째 문제 : 코드 WorkerSessionCreator 사용합니다. 한 대의 기계가 수석이어야합니다. 이 경우 ChiefSessionCreator을 사용해야합니다. tf.train.MonitoredTrainingSession 다음을 사용하는 것이 좋습니다.

세 번째 문제 : sess.should_stop()은 각각 run 앞에 표시되어야합니다.