Python tensorflow.python.framework.errors.DeadlineExceededError() Examples
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code examples of tensorflow.python.framework.errors.DeadlineExceededError().
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Example #1
| Source File: session_manager_test.py From deep_image_model with Apache License 2.0 | 6 votes |
|
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)
Example #2
| Source File: session_manager_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
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)
Example #3
| Source File: session_manager_test.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def testWaitForSessionReturnsNoneAfterTimeout(self):
with tf.Graph().as_default():
tf.Variable(1, name="v")
sm = tf.train.SessionManager(ready_op=tf.assert_variables_initialized(),
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)
Example #4
| Source File: session_manager.py From lambda-packs with MIT License | 4 votes |
|
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)
Example #5
| Source File: session_manager.py From auto-alt-text-lambda-api with MIT License | 4 votes |
|
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)
Example #6
| Source File: session_manager.py From deep_image_model with Apache License 2.0 | 4 votes |
|
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)
Example #7
| Source File: session_manager.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
|
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)
Example #8
| Source File: session_manager.py From keras-lambda with MIT License | 4 votes |
|
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)
