def _train_model(self,
input_fn,
steps,
feed_fn=None,
init_op=None,
init_feed_fn=None,
init_fn=None,
device_fn=None,
monitors=None,
log_every_steps=100,
fail_on_nan_loss=True,
max_steps=None):
# TODO(wicke): Remove this once Model and associated code are gone.
if hasattr(self._config, 'execution_mode'):
if self._config.execution_mode not in ('all', 'train'):
return
# Stagger startup of worker sessions based on task id.
sleep_secs = min(
self._config.training_worker_max_startup_secs,
self._config.task *
self._config.training_worker_session_startup_stagger_secs)
if sleep_secs:
logging.info('Waiting %d secs before starting task %d.', sleep_secs,
self._config.task)
time.sleep(sleep_secs)
# Device allocation
device_fn = device_fn or self._device_fn
self._graph = ops.Graph()
with self._graph.as_default() as g, g.device(device_fn):
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
train_op, loss_op = self._get_train_ops(features, targets)
# Add default monitors.
if monitors is None:
monitors = []
hooks = [m for m in monitors
if isinstance(m, session_run_hook.SessionRunHook)]
deprecated_monitors = [
m for m in monitors
if not isinstance(m, session_run_hook.SessionRunHook)
]
supervisor_is_chief = self._config.is_chief
if not supervisor_is_chief:
# Prune list of monitor to the ones runnable on all workers.
deprecated_monitors = [m for m in deprecated_monitors
if m.run_on_all_workers]
# Setup monitors.
for monitor in deprecated_monitors:
monitor.set_estimator(self)
if deprecated_monitors:
hooks.append(monitor_lib.RunHookAdapterForMonitors(deprecated_monitors))
return graph_actions._monitored_train( # pylint: disable=protected-access
graph=g,
output_dir=self._model_dir,
train_op=train_op,
loss_op=loss_op,
global_step_tensor=global_step,
init_op=init_op,
init_feed_dict=init_feed_fn() if init_feed_fn is not None else None,
init_fn=init_fn,
log_every_steps=log_every_steps,
supervisor_is_chief=supervisor_is_chief,
supervisor_master=self._config.master,
supervisor_save_model_secs=self._config.save_checkpoints_secs,
supervisor_save_summaries_steps=self._config.save_summary_steps,
keep_checkpoint_max=self._config.keep_checkpoint_max,
feed_fn=feed_fn,
steps=steps,
fail_on_nan_loss=fail_on_nan_loss,
hooks=hooks,
max_steps=max_steps)
def _train_model(self,
input_fn,
steps,
feed_fn=None,
init_op=None,
init_feed_fn=None,
init_fn=None,
device_fn=None,
monitors=None,
log_every_steps=100,
fail_on_nan_loss=True,
max_steps=None):
# TODO(wicke): Remove this once Model and associated code are gone.
if hasattr(self._config, 'execution_mode'):
if self._config.execution_mode not in ('all', 'train'):
return
# Stagger startup of worker sessions based on task id.
sleep_secs = min(
self._config.training_worker_max_startup_secs,
self._config.task *
self._config.training_worker_session_startup_stagger_secs)
if sleep_secs:
logging.info('Waiting %d secs before starting task %d.', sleep_secs,
self._config.task)
time.sleep(sleep_secs)
# Device allocation
device_fn = device_fn or self._device_fn
self._graph = ops.Graph()
with self._graph.as_default() as g, g.device(device_fn):
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, labels = input_fn()
self._check_inputs(features, labels)
# The default return type of _get_train_ops is ModelFnOps. But there are
# some subclasses of tf.contrib.learn.Estimator which override this
# method and use the legacy signature, namely _get_train_ops returns a
# (train_op, loss) tuple. The following else-statement code covers these
# cases, but will soon be deleted after the subclasses are updated.
# TODO(b/32664904): Update subclasses and delete the else-statement.
train_ops = self._get_train_ops(features, labels)
if isinstance(train_ops, ModelFnOps): # Default signature
train_op = train_ops.train_op
loss_op = train_ops.loss
else: # Legacy signature
if len(train_ops) != 2:
raise ValueError('Expected a tuple of train_op and loss, got {}'.
format(train_ops))
train_op = train_ops[0]
loss_op = train_ops[1]
hooks = monitor_lib.replace_monitors_with_hooks(monitors, self)
ops.add_to_collection(ops.GraphKeys.LOSSES, loss_op)
return graph_actions._monitored_train( # pylint: disable=protected-access
graph=g,
output_dir=self._model_dir,
train_op=train_op,
loss_op=loss_op,
global_step_tensor=global_step,
init_op=init_op,
init_feed_dict=init_feed_fn() if init_feed_fn is not None else None,
init_fn=init_fn,
log_every_steps=log_every_steps,
supervisor_is_chief=self.config.is_chief,
supervisor_master=self._config.master,
supervisor_save_model_secs=self._config.save_checkpoints_secs,
supervisor_save_model_steps=self._config.save_checkpoints_steps,
supervisor_save_summaries_steps=self._config.save_summary_steps,
keep_checkpoint_max=self._config.keep_checkpoint_max,
feed_fn=feed_fn,
steps=steps,
fail_on_nan_loss=fail_on_nan_loss,
hooks=hooks,
max_steps=max_steps)
def _train_model(self,
input_fn,
steps,
feed_fn=None,
init_op=None,
init_feed_fn=None,
init_fn=None,
device_fn=None,
monitors=None,
log_every_steps=100,
fail_on_nan_loss=True,
max_steps=None):
# TODO(wicke): Remove this once Model and associated code are gone.
if hasattr(self._config, 'execution_mode'):
if self._config.execution_mode not in ('all', 'train'):
return
# Stagger startup of worker sessions based on task id.
sleep_secs = min(
self._config.training_worker_max_startup_secs,
self._config.task *
self._config.training_worker_session_startup_stagger_secs)
if sleep_secs:
logging.info('Waiting %d secs before starting task %d.',
sleep_secs, self._config.task)
time.sleep(sleep_secs)
# Device allocation
device_fn = device_fn or self._device_fn
self._graph = ops.Graph()
with self._graph.as_default() as g, g.device(device_fn):
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, targets = input_fn()
self._check_inputs(features, targets)
train_op, loss_op = self._get_train_ops(features, targets)
# Add default monitors.
if monitors is None:
monitors = []
hooks = [
m for m in monitors
if isinstance(m, session_run_hook.SessionRunHook)
]
deprecated_monitors = [
m for m in monitors
if not isinstance(m, session_run_hook.SessionRunHook)
]
supervisor_is_chief = self._config.is_chief
if not supervisor_is_chief:
# Prune list of monitor to the ones runnable on all workers.
deprecated_monitors = [
m for m in deprecated_monitors if m.run_on_all_workers
]
# Setup monitors.
for monitor in deprecated_monitors:
monitor.set_estimator(self)
if deprecated_monitors:
hooks.append(
monitor_lib.RunHookAdapterForMonitors(deprecated_monitors))
ops.add_to_collection(ops.GraphKeys.LOSSES, loss_op)
return graph_actions._monitored_train( # pylint: disable=protected-access
graph=g,
output_dir=self._model_dir,
train_op=train_op,
loss_op=loss_op,
global_step_tensor=global_step,
init_op=init_op,
init_feed_dict=init_feed_fn()
if init_feed_fn is not None else None,
init_fn=init_fn,
log_every_steps=log_every_steps,
supervisor_is_chief=supervisor_is_chief,
supervisor_master=self._config.master,
supervisor_save_model_secs=self._config.save_checkpoints_secs,
supervisor_save_model_steps=self._config.
save_checkpoints_steps,
supervisor_save_summaries_steps=self._config.
save_summary_steps,
keep_checkpoint_max=self._config.keep_checkpoint_max,
feed_fn=feed_fn,
steps=steps,
fail_on_nan_loss=fail_on_nan_loss,
hooks=hooks,
max_steps=max_steps)
def _train_model(self,
input_fn,
steps,
feed_fn=None,
init_op=None,
init_feed_fn=None,
init_fn=None,
device_fn=None,
monitors=None,
log_every_steps=100,
fail_on_nan_loss=True,
max_steps=None):
# TODO(wicke): Remove this once Model and associated code are gone.
if hasattr(self._config, 'execution_mode'):
if self._config.execution_mode not in ('all', 'train'):
return
# Stagger startup of worker sessions based on task id.
sleep_secs = min(
self._config.training_worker_max_startup_secs,
self._config.task_id *
self._config.training_worker_session_startup_stagger_secs)
if sleep_secs:
logging.info('Waiting %d secs before starting task %d.', sleep_secs,
self._config.task_id)
time.sleep(sleep_secs)
# Device allocation
device_fn = device_fn or self._device_fn
self._graph = ops.Graph()
with self._graph.as_default() as g, g.device(device_fn):
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, labels = input_fn()
self._check_inputs(features, labels)
# The default return type of _get_train_ops is ModelFnOps. But there are
# some subclasses of tf.contrib.learn.Estimator which override this
# method and use the legacy signature, namely _get_train_ops returns a
# (train_op, loss) tuple. The following else-statement code covers these
# cases, but will soon be deleted after the subclasses are updated.
# TODO(b/32664904): Update subclasses and delete the else-statement.
train_ops = self._get_train_ops(features, labels)
if isinstance(train_ops, model_fn_lib.ModelFnOps): # Default signature
train_op = train_ops.train_op
loss_op = train_ops.loss
else: # Legacy signature
if len(train_ops) != 2:
raise ValueError('Expected a tuple of train_op and loss, got {}'.
format(train_ops))
train_op = train_ops[0]
loss_op = train_ops[1]
hooks = monitor_lib.replace_monitors_with_hooks(monitors, self)
ops.add_to_collection(ops.GraphKeys.LOSSES, loss_op)
return graph_actions._monitored_train( # pylint: disable=protected-access
graph=g,
output_dir=self._model_dir,
train_op=train_op,
loss_op=loss_op,
global_step_tensor=global_step,
init_op=init_op,
init_feed_dict=init_feed_fn() if init_feed_fn is not None else None,
init_fn=init_fn,
log_every_steps=log_every_steps,
supervisor_is_chief=self.config.is_chief,
supervisor_master=self._config.master,
supervisor_save_model_secs=self._config.save_checkpoints_secs,
supervisor_save_model_steps=self._config.save_checkpoints_steps,
supervisor_save_summaries_steps=self._config.save_summary_steps,
keep_checkpoint_max=self._config.keep_checkpoint_max,
feed_fn=feed_fn,
steps=steps,
fail_on_nan_loss=fail_on_nan_loss,
hooks=hooks,
max_steps=max_steps)