def parse_file(self, directory):
mls = []
for f in self.parser.find_all_files(directory):
if os.path.isdir(f):
continue
try:
self.logger.info(f + " will be parsed.")
mls.extend(self.parser.parse_summary(f, self.metrics))
except Exception as e:
self.logger.warning("Unexpected error: " + str(e))
continue
# Metrics logs must contain at least one objective metric value
# Objective metric is located at first index
is_objective_metric_reported = False
for ml in mls:
if ml.metric.name == self.metrics[0]:
is_objective_metric_reported = True
break
# If objective metrics were not reported, insert unavailable value in the DB
if not is_objective_metric_reported:
mls = [
api_pb2.MetricLog(time_stamp=rfc3339.rfc3339(datetime.now()),
metric=api_pb2.Metric(
name=self.metrics[0],
value=const.UNAVAILABLE_METRIC_VALUE))
]
self.logger.info(
"Objective metric {} is not found in training logs, {} value is reported"
.format(self.metrics[0], const.UNAVAILABLE_METRIC_VALUE))
return api_pb2.ObservationLog(metric_logs=mls)
def parse_file(self, directory):
mls = []
for f in self.parser.find_all_files(directory):
if os.path.isdir(f):
continue
try:
self.logger.info(f + " will be parsed.")
mls = self.parser.parse_summary(f, self.metrics)
except Exception as e:
self.logger.warning("Unexpected error: " + str(e))
continue
return api_pb2.ObservationLog(metric_logs=mls)
class MetricsCollector:
def __init__(self, metric_names):
self.logger = getLogger(__name__)
handler = StreamHandler()
handler.setLevel(INFO)
self.logger.setLevel(INFO)
self.logger.addHandler(handler)
self.logger.propagate = False
self.metrics = metric_names
self.parser = TFEventFileParser()
def parse_file(self, directory):
mls = []
for f in self.parser.find_all_files(directory):
if tf.gfile.IsDirectory(f):
continue
try:
self.logger.info(f + " will be parsed.")
mls.extend(self.parser.parse_summary(f, self.metrics))
except Exception, e:
self.logger.warning("Unexpected error: " + str(e))
continue
return api_pb2.ObservationLog(metric_logs=mls)