def make_session_end_summary(status: str, end_time_secs: Optional[int] = None):
"""
Args:
status: outcome of this run, one of of 'UNKNOWN', 'SUCCESS', 'FAILURE', 'RUNNING'
end_time_secs: optional ending time in seconds
Returns:
"""
status = Status.DESCRIPTOR.values_by_name[
f"STATUS_{status.upper()}"].number
if end_time_secs is None:
import time
end_time_secs = int(time.time())
session_end_summary = SessionEndInfo(status=status,
end_time_secs=end_time_secs)
session_end_content = HParamsPluginData(
session_end_info=session_end_summary, version=PLUGIN_DATA_VERSION)
session_end_summary_metadata = SummaryMetadata(
plugin_data=SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME,
content=session_end_content.SerializeToString()))
session_end_summary = Summary(value=[
Summary.Value(tag=SESSION_END_INFO_TAG,
metadata=session_end_summary_metadata)
])
return session_end_summary
def make_session_start_summary(
hparam_values,
group_name: Optional[str] = None,
start_time_secs: Optional[int] = None,
):
"""Assign values to the hyperparameters in the context of this session.
Args:
hparam_values: a dict of ``hp_name`` -> ``hp_value`` mappings
group_name: optional group name for this session
start_time_secs: optional starting time in seconds
Returns:
"""
if start_time_secs is None:
import time
start_time_secs = int(time.time())
session_start_info = SessionStartInfo(group_name=group_name,
start_time_secs=start_time_secs)
for hp_name, hp_value in hparam_values.items():
# Logging a None would raise an exception when setting session_start_info.hparams[hp_name].number_value = None.
# Logging a float.nan instead would work, but that run would not show at all in the tensorboard hparam plugin.
# The best thing to do here is to skip that value, it will show as a blank cell in the table view of the
# tensorboard plugin. However, that run would not be shown in the parallel coord or in the scatter plot view.
if hp_value is None:
loguru.warning(
f"Hyper parameter {hp_name} is `None`: the tensorboard hp plugin "
f"will show this run in table view, but not in parallel coordinates "
f"view or in scatter plot matrix view")
continue
if isinstance(hp_value, string_types):
session_start_info.hparams[hp_name].string_value = hp_value
continue
if isinstance(hp_value, bool):
session_start_info.hparams[hp_name].bool_value = hp_value
continue
if not isinstance(hp_value, (int, float)):
hp_value = make_np(hp_value)[0]
session_start_info.hparams[hp_name].number_value = hp_value
session_start_content = HParamsPluginData(
session_start_info=session_start_info, version=PLUGIN_DATA_VERSION)
session_start_summary_metadata = SummaryMetadata(
plugin_data=SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME,
content=session_start_content.SerializeToString()))
session_start_summary = Summary(value=[
Summary.Value(tag=SESSION_START_INFO_TAG,
metadata=session_start_summary_metadata)
])
return session_start_summary
def hparams(hparam_dict=None, metric_dict=None):
from tensorboardX.proto.plugin_hparams_pb2 import HParamsPluginData, SessionEndInfo, SessionStartInfo
from tensorboardX.proto.api_pb2 import Experiment, HParamInfo, MetricInfo, MetricName, Status, DataType
from six import string_types
PLUGIN_NAME = 'hparams'
PLUGIN_DATA_VERSION = 0
EXPERIMENT_TAG = '_hparams_/experiment'
SESSION_START_INFO_TAG = '_hparams_/session_start_info'
SESSION_END_INFO_TAG = '_hparams_/session_end_info'
# TODO: expose other parameters in the future.
# hp = HParamInfo(name='lr',display_name='learning rate', type=DataType.DATA_TYPE_FLOAT64, domain_interval=Interval(min_value=10, max_value=100)) # noqa E501
# mt = MetricInfo(name=MetricName(tag='accuracy'), display_name='accuracy', description='', dataset_type=DatasetType.DATASET_VALIDATION) # noqa E501
# exp = Experiment(name='123', description='456', time_created_secs=100.0, hparam_infos=[hp], metric_infos=[mt], user='******') # noqa E501
hps = []
ssi = SessionStartInfo()
for k, v in hparam_dict.items():
if isinstance(v, string_types):
ssi.hparams[k].string_value = v
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_STRING))
continue
if isinstance(v, bool):
ssi.hparams[k].bool_value = v
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_BOOL))
continue
if not isinstance(v, int) or not isinstance(v, float):
v = make_np(v)[0]
ssi.hparams[k].number_value = v
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_FLOAT64))
continue
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_UNSET))
content = HParamsPluginData(session_start_info=ssi, version=PLUGIN_DATA_VERSION)
smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
ssi = Summary(value=[Summary.Value(tag=SESSION_START_INFO_TAG, metadata=smd)])
mts = [MetricInfo(name=MetricName(tag=k)) for k in metric_dict.keys()]
exp = Experiment(hparam_infos=hps, metric_infos=mts)
content = HParamsPluginData(experiment=exp, version=PLUGIN_DATA_VERSION)
smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
exp = Summary(value=[Summary.Value(tag=EXPERIMENT_TAG, metadata=smd)])
sei = SessionEndInfo(status=Status.STATUS_SUCCESS)
content = HParamsPluginData(session_end_info=sei, version=PLUGIN_DATA_VERSION)
smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
sei = Summary(value=[Summary.Value(tag=SESSION_END_INFO_TAG, metadata=smd)])
return exp, ssi, sei
def make_experiment_summary(hparam_infos, metric_infos, experiment):
"""Define hyperparameters and metrics.
Args:
hparam_infos: information about all hyperparameters (name, description, type etc.),
list of dicts containing 'name' (required), 'type', 'description', 'display_name',
'domain_discrete', 'domain_interval'
metric_infos: information about all metrics (tag, description etc.),
list of dicts containing 'tag' (required), 'dataset_type', 'description', 'display_name'
experiment: dict containing 'name' (required), 'description', 'time_created_secs', 'user'
Returns:
"""
def make_hparam_info(hparam):
data_type = hparam.get("type")
if hparam.get("type") is None:
data_type = DataType.DATA_TYPE_UNSET
elif hparam.get("type") in string_types:
data_type = DataType.DATA_TYPE_STRING
elif hparam.get("type") is bool:
data_type = DataType.DATA_TYPE_BOOL
elif hparam.get("type") in (float, int):
data_type = DataType.DATA_TYPE_FLOAT64
return HParamInfo(
name=hparam["name"],
type=data_type,
description=hparam.get("description"),
display_name=hparam.get("display_name"),
domain_discrete=hparam.get("domain_discrete"),
domain_interval=hparam.get("domain_interval"),
)
def make_metric_info(metric):
return MetricInfo(
name=MetricName(tag=metric["tag"]),
dataset_type=DatasetType.Value(
f'DATASET_{metric.get("dataset_type", "UNKNOWN").upper()}'),
description=metric.get("description"),
display_name=metric.get("display_name"),
)
def make_experiment_info(experiment, metric_infos, hparam_infos):
return Experiment(
name=experiment["name"],
description=experiment.get("description"),
time_created_secs=experiment.get("time_created_secs"),
user=experiment.get("user"),
metric_infos=metric_infos,
hparam_infos=hparam_infos,
)
metric_infos = [make_metric_info(m) for m in metric_infos]
hparam_infos = [make_hparam_info(hp) for hp in hparam_infos]
experiment = make_experiment_info(experiment, metric_infos, hparam_infos)
experiment_content = HParamsPluginData(experiment=experiment,
version=PLUGIN_DATA_VERSION)
experiment_summary_metadata = SummaryMetadata(
plugin_data=SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME,
content=experiment_content.SerializeToString()))
experiment_summary = Summary(value=[
Summary.Value(tag=EXPERIMENT_TAG, metadata=experiment_summary_metadata)
])
return experiment_summary