Python errors.DeadlineExceededError方法代码示例

# 需要导入模块: from tensorflow.python.framework import errors [as 别名]
# 或者: from tensorflow.python.framework.errors import DeadlineExceededError [as 别名]
def testWaitForSessionWithReadyForLocalInitOpFailsToReadyLocal(self):
    with tf.Graph().as_default() as graph:
      v = tf.Variable(1, name="v")
      w = tf.Variable(
          v,
          trainable=False,
          collections=[tf.GraphKeys.LOCAL_VARIABLES],
          name="w")
      sm = tf.train.SessionManager(
          graph=graph,
          ready_op=tf.report_uninitialized_variables(),
          ready_for_local_init_op=tf.report_uninitialized_variables(),
          local_init_op=w.initializer)

      with self.assertRaises(tf.errors.DeadlineExceededError):
        # Time-out because w fails to be initialized,
        # because of overly restrictive ready_for_local_init_op
        sm.wait_for_session("", max_wait_secs=3) 

# 需要导入模块: from tensorflow.python.framework import errors [as 别名]
# 或者: from tensorflow.python.framework.errors import DeadlineExceededError [as 别名]
def testWaitForSessionReturnsNoneAfterTimeout(self):
    with tf.Graph().as_default():
      tf.Variable(1, name="v")
      sm = tf.train.SessionManager(ready_op=tf.report_uninitialized_variables(),
                                   recovery_wait_secs=1)

      # Set max_wait_secs to allow us to try a few times.
      with self.assertRaises(errors.DeadlineExceededError):
        sm.wait_for_session(master="", max_wait_secs=3) 

# 需要导入模块: from tensorflow.python.framework import errors [as 别名]
# 或者: from tensorflow.python.framework.errors import DeadlineExceededError [as 别名]
def wait_for_session(self, master, config=None, max_wait_secs=float("Inf")):
    """Creates a new `Session` and waits for model to be ready.

    Creates a new `Session` on 'master'.  Waits for the model to be
    initialized or recovered from a checkpoint.  It's expected that
    another thread or process will make the model ready, and that this
    is intended to be used by threads/processes that participate in a
    distributed training configuration where a different thread/process
    is responsible for initializing or recovering the model being trained.

    NB: The amount of time this method waits for the session is bounded
    by max_wait_secs. By default, this function will wait indefinitely.

    Args:
      master: `String` representation of the TensorFlow master to use.
      config: Optional ConfigProto proto used to configure the session.
      max_wait_secs: Maximum time to wait for the session to become available.

    Returns:
      A `Session`. May be None if the operation exceeds the timeout
      specified by config.operation_timeout_in_ms.

    Raises:
      tf.DeadlineExceededError: if the session is not available after
        max_wait_secs.
    """
    self._target = master

    if max_wait_secs is None:
      max_wait_secs = float("Inf")
    timer = _CountDownTimer(max_wait_secs)

    while True:
      sess = session.Session(self._target, graph=self._graph, config=config)
      not_ready_msg = None
      not_ready_local_msg = None
      local_init_success, not_ready_local_msg = self._try_run_local_init_op(
          sess)
      if local_init_success:
        # Successful if local_init_op is None, or ready_for_local_init_op passes
        is_ready, not_ready_msg = self._model_ready(sess)
        if is_ready:
          return sess

      self._safe_close(sess)

      # Do we have enough time left to try again?
      remaining_ms_after_wait = (
          timer.secs_remaining() - self._recovery_wait_secs)
      if remaining_ms_after_wait < 0:
        raise errors.DeadlineExceededError(
            None, None,
            "Session was not ready after waiting %d secs." % (max_wait_secs,))

      logging.info("Waiting for model to be ready.  "
                   "Ready_for_local_init_op:  %s, ready: %s",
                   not_ready_local_msg, not_ready_msg)
      time.sleep(self._recovery_wait_secs)