def CreateRunners(self, jobs, logdir, trial=base_trial.NoOpTrial()):
"""Creates a list of runners based on `FLAGS.mode`.
Args:
jobs: a list of runner jobs.
logdir: the directory used for logging, usually on CNS.
trial: optional `Trial` object, used for reporting measures and early
stopping.
Returns:
A list of `.BaseRunner`, one per job in `jobs`.
"""
all_runners = []
is_training = 'trainer' in jobs or 'trainer_client' in jobs
for j in jobs:
tf_master = FLAGS.tf_master
# Ensure that decoder or evaler threads do not clobber variables being
# updated by trainer by forcing them to use independent sessions.
if is_training and (j.startswith('decoder')
or j.startswith('evaler')):
tf_master = ''
runner = self._CreateRunner(j, FLAGS.model_task_name, logdir,
tf_master, trial)
all_runners.append(runner)
return all_runners
def __init__(self,
params,
model_task_name,
logdir,
tf_master,
trial=base_trial.NoOpTrial()):
"""Construct a new BaseRunner.
Args:
params: Params object containing model configuration.
model_task_name: String name of the task this runner should execute for
multitask models only. See flag for details.
logdir: String path to the log directory to output to.
tf_master: String path to the master job, e.g. 'local'.
trial: An optional hyperparameter trial. Used by Vizier studies.
"""
p = params.Copy()
# Set in subclasses.
self._job_name = ''
self._params = trial.OverrideModelParams(p)
tf.logging.info('=' * 60)
for line in self.params.ToText().split('\n'):
tf.logging.info('%s', line)
tf.logging.info('=' * 60)
self._logdir = logdir
self._tf_master = tf_master
self._model_task_name = model_task_name
self._trial = trial
# If the runner is conducting a Vizier trial, scope all the variables
# (e.g., global_step) by the trial id so that we do not share states across
# trials.
self._container_id = self._trial.Name()
self._should_report_metrics = False
# To early terminate a runner, we set max_steps here and that will trigger
# appropriate ShouldStop behavior in the threads. This is used by Vizier
# to early stop a trial.
self._max_steps = None
self.params.cluster.logdir = logdir
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._train_dir = os.path.join(self._logdir, 'train')
tf.io.gfile.makedirs(self._train_dir)
self._graph = tf.Graph()
self._summary_writer = None
self._initialize_tables = None
self._dequeue_thread_complete = False
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
self._early_stop = None
if p.train.early_stop and p.train.early_stop.window:
self._early_stop = early_stop.EarlyStop(p.train.early_stop)
with self._graph.as_default():
self._early_stop.FProp(None)
self._init_input_ops = []
self._SetStatusMessage('Starting ...')
def CreateRunners(cls, jobs, model_name, logdir,
trial=base_trial.NoOpTrial()):
"""Creates a list of runners based on `FLAGS.mode`.
Args:
jobs: a list of runner jobs.
model_name: name of a registered `ModelParams` class.
logdir: the directory used for logging, usually on CNS.
trial: optional `Trial` object, used for reporting measures and early
stopping.
Returns:
A list of `.BaseRunner`, one per job in `jobs`.
"""
runners = []
for j in jobs:
tf_master = FLAGS.tf_master
# Ensure that decoder or evaler threads do not clobber variables being
# updated by trainer by forcing them to use independent sessions.
if ('trainer' in jobs and
(j.startswith('decoder') or j.startswith('evaler'))):
tf_master = ''
runner = cls._CreateRunner(j, model_name, FLAGS.model_task_name, logdir,
tf_master, trial)
runners.append(runner)
return runners
def __init__(self,
params,
model_task_name,
logdir,
tf_master,
trial=base_trial.NoOpTrial()):
"""Construct a new BaseRunner.
Args:
params: Params object containing model configuration.
model_task_name: String name of the task this runner should execute
for multitask models only. See flag for details.
logdir: String path to the log directory to output to.
tf_master: String path to the master job, e.g. 'local'.
trial: An optional hyperparameter trial. Used by Vizier studies.
"""
p = params.Copy()
p.add_summary = FLAGS.add_summary
self._params = trial.OverrideModelParams(p)
tf.logging.info('=' * 60)
for line in self.params.ToText().split('\n'):
tf.logging.info('%s', line)
tf.logging.info('=' * 60)
self._logdir = logdir
self._tf_master = tf_master
self._model_task_name = model_task_name
self._trial = trial
# If the runner is conducting a Vizier trial, scope all the variables
# (e.g., global_step) by the trial id so that we do not share states across
# trials.
self._container_id = self._trial.Name()
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._train_dir = os.path.join(self._logdir, 'train')
self._graph = tf.Graph()
self._summary_writer = None
self.initialize_tables = None
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
self._early_stop = None
if p.train.early_stop and p.train.early_stop.window:
self._early_stop = early_stop.EarlyStop(p.train.early_stop)
with self._graph.as_default():
self._early_stop.FProp(None)
self._SetStatusMessage('Starting ...')
def __init__(self, *args, **kwargs):
super(BaseTrainerTest, self).__init__(*args, **kwargs)
self._trial = base_trial.NoOpTrial()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._trial = base_trial.NoOpTrial()
def __init__(self,
params,
model_task_name='',
logdir='',
tf_master='',
trial=base_trial.NoOpTrial()):
"""A job runner.
Args:
params: Params object containing model configuration.
model_task_name: String name of the task this runner should execute for
multitask models only. See flag for details.
logdir: String path to the log directory to output to.
tf_master: String path to the master job, e.g. 'local'.
trial: An optional hyperparameter trial. Used by Vizier studies.
"""
self._params = trial.OverrideModelParams(params.Copy())
p = self.params
self._model_task_name = model_task_name
self._logdir = logdir
self._train_dir = os.path.join(self._logdir, 'train')
tf.io.gfile.makedirs(self._train_dir)
self._tf_master = tf_master
self._trial = trial
# If the runner is conducting a Vizier trial, scope all the variables
# (e.g., global_step) by the trial id so that we do not share states across
# trials.
self._container_id = self._trial.Name()
# Set in subclasses.
self._job_name = ''
self._daemon = False
self._verbose_enqueue_logging = False
self._checkpointer = None
self._should_report_metrics = False
if py_utils.IsEagerMode():
self._graph = None
else:
self._graph = tf.Graph()
self._summary_writer = None
self._initialize_tables = None
self._dequeue_thread_complete = False
self._early_stop = None
# The actual EarlyStop object.
if p.train.early_stop and p.train.early_stop.window:
early_stop.MetricHistory.SetLogdirInMetricHistories(
p, self._logdir)
self._early_stop = p.train.early_stop.Instantiate()
self._verbose_enqueue_logging = True
# Merged TF scalar summaries for training related input data stats.
self._merged_input_data_summary_op = None
# To early terminate a runner, we set max_steps here and that will trigger
# appropriate ShouldStop behavior in the threads. This is used by Vizier
# to early stop a trial and also EarlyStop to stop training based on
# metrics.
self._max_steps_for_early_stop = None
self.enqueue_ops = None
tf.logging.info('=' * 60)
for line in self.params.ToText().split('\n'):
tf.logging.info('%s', line)
tf.logging.info('=' * 60)
self._SetStatusMessage('Starting ...')
self.params.cluster.logdir = logdir
self._cluster = cluster_factory.Cluster(self.params.cluster)
self._worker_cluster_def = self._cluster.worker_cluster_def
if py_utils.IsEagerMode():
self._cluster.InitDevicesEager()
else:
self._cluster.InitDevices(self._GetSession())
# Ensure global step tensor is created.
with contextlib.ExitStack() as stack:
if not py_utils.IsEagerMode():
stack.enter_context(self._graph.as_default())
stack.enter_context(tf.device(self._cluster.GetPlacer()))
# It is important that we enter the tf.container scope *after*
# the graph scope. If we reverse the ordering, the tf.container
# basically has no-effect which is a tricky silent error.
stack.enter_context(tf.container(self._container_id))
self._global_step_var = py_utils.GetOrCreateGlobalStepVar()
