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_summary(self, tfefile):
metric_logs = []
event_accumulator = EventAccumulator(tfefile,
size_guidance={'tensors': 0})
event_accumulator.Reload()
for tag in event_accumulator.Tags()['tensors']:
for m in self.metric_names:
tfefile_parent_dir = os.path.dirname(m) if len(
m.split("/")) >= 2 else os.path.dirname(tfefile)
basedir_name = os.path.dirname(tfefile)
if not tag.startswith(
m.split("/")[-1]) or not basedir_name.endswith(
tfefile_parent_dir):
continue
for wall_time, step, tensor in event_accumulator.Tensors(tag):
ml = api_pb2.MetricLog(time_stamp=rfc3339.rfc3339(
datetime.fromtimestamp(wall_time)),
metric=api_pb2.Metric(
name=m,
value=str(
tf.make_ndarray(tensor))))
metric_logs.append(ml)
return metric_logs
def parse_summary(self, tfefile, metrics):
metric_logs = []
for summary in tf.train.summary_iterator(tfefile):
paths = tfefile.split("/")
for v in summary.summary.value:
for m in metrics:
tag = str(v.tag)
if len(paths) >= 2 and len(m.split("/")) >= 2:
tag = str(paths[-2] + "/" + v.tag)
if tag.startswith(m):
ml = api_pb2.MetricLog(time_stamp=rfc3339.rfc3339(
datetime.fromtimestamp(summary.wall_time)),
metric=api_pb2.Metric(
name=m,
value=str(v.simple_value)))
metric_logs.append(ml)
return metric_logs
def register_trial(stub):
try:
obj = api_pb2.ObjectiveSpec(type=1, goal=0.09, objective_metric_name="loss")
parameters = api_pb2.TrialSpec.ParameterAssignments(assignments=[api_pb2.ParameterAssignment(name="rl", value="0.01")])
spec = api_pb2.TrialSpec(experiment_name=TEST_EXPERIMENT,
objective=obj,
run_spec="a batch/job resource",
metrics_collector_spec="metrics/collector",
parameter_assignments=parameters)
observation = api_pb2.Observation(metrics=[api_pb2.Metric(name="loss", value="0.54")])
status = api_pb2.TrialStatus(condition=2,
observation=observation,
start_time="2019-04-28T17:09:15Z",
completion_time="2019-04-28T18:09:15Z")
t = api_pb2.Trial(name=TEST_TRIAL, status=status, spec=spec)
stub.RegisterTrial(api_pb2.RegisterTrialRequest(trial=t), 10)
logger.info("Register trial %s successfully" % TEST_TRIAL)
except:
logger.error("Failed to register trial %s" % TEST_TRIAL, exc_info=True)
raise
