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 testEarlyStopDefaultIsNoOp(self):
p = early_stop.EarlyStop.Params()
es = early_stop.EarlyStop(p)
es.FProp(None)
mh = early_stop.MetricHistory
a = mh.ConditionalAppend(es.params.metric_history.jobname,
es.params.metric_history.metric, 1, 10.0)
s = es.Stop(None)
self.assertFalse(a)
self.assertFalse(s)
self.assertIsNone(es._node)
self.assertEqual(len(early_stop.MetricHistory._metric_histories_map), 0)
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 testEarlyStoppingAscendingTfEvents(self):
logdir = test_helper.test_src_dir_path('core/ops')
p = early_stop.EarlyStop.Params()
p.window = 1000
p.tolerance = 0.0
p.metric_history.local_filesystem = True
p.metric_history.minimize = False
p.metric_history.jobname = 'testdata'
p.metric_history.metric = 'bleu/dev'
p.metric_history.tfevent_file = True
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
es = early_stop.EarlyStop(p)
es.FProp(None)
with self.session() as sess:
self.assertTrue(es.Stop(sess))
self.assertEqual(es.best_step, 102600)
self.assertEqual(es.last_step, 185200)
def testEarlyStoppingAscendingMetric(self):
logdir = tf.test.get_temp_dir()
tf.gfile.MkDir(os.path.join(logdir, 'decoder_dev'))
p = early_stop.EarlyStop.Params()
p.window = 2
p.tolerance = 1.0
p.metric_history.local_filesystem = True
p.metric_history.minimize = False
p.metric_history.jobname = 'decoder_dev'
p.metric_history.metric = 'canonical_bleu'
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
es = early_stop.EarlyStop(p)
es.FProp(None)
with self.session() as sess:
jobname = es.metric_history.params.jobname
metric = es.metric_history.params.metric
mh = early_stop.MetricHistory
mh.ConditionalAppend(jobname, metric, 1, 0.0)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 1)
self.assertEqual(es.last_step, 1)
mh.ConditionalAppend(jobname, metric, 2, 1.0)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 1)
self.assertEqual(es.last_step, 2)
mh.ConditionalAppend(jobname, metric, 3, 2.5)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 3)
self.assertEqual(es.last_step, 3)
mh.ConditionalAppend(jobname, metric, 5, 2.0)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 3)
self.assertEqual(es.last_step, 5)
mh.ConditionalAppend(jobname, metric, 6, 1.0)
self.assertTrue(es.Stop(sess))
self.assertEqual(es.best_step, 3)
self.assertEqual(es.last_step, 6)
def testEarlyStopping(self):
logdir = tf.test.get_temp_dir()
tf.gfile.MkDir(os.path.join(logdir, 'eval_dev'))
p = early_stop.EarlyStop.Params()
p.window = 2
p.tolerance = 1.0
p.metric_history.local_filesystem = True
early_stop.MetricHistory.SetLogdirInMetricHistories(p, logdir)
es = early_stop.EarlyStop(p)
es.FProp(None)
with self.session() as sess:
jobname = es.metric_history.params.jobname
metric = es.metric_history.params.metric
mh = early_stop.MetricHistory
mh.ConditionalAppend(jobname, metric, 1, 10.0)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 1)
self.assertEqual(es.last_step, 1)
mh.ConditionalAppend(jobname, metric, 2, 5.0)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 2)
self.assertEqual(es.last_step, 2)
mh.ConditionalAppend(jobname, metric, 3, 4.0)
self.assertFalse(es.Stop(sess))
self.assertEqual(es.best_step, 2)
self.assertEqual(es.last_step, 3)
mh.ConditionalAppend(jobname, metric, 5, 4.0)
self.assertTrue(es.Stop(sess))
self.assertEqual(es.best_step, 2)
self.assertEqual(es.last_step, 5)
