def get_logger(name=None):
if EXPERIMENT is None:
print("default logger")
return logging.getLogger(name)
elif EXPERIMENT.run_logger is None:
EXPERIMENT.logger = create_basic_stream_logger()
return EXPERIMENT.run_logger
def initialize_logging(experiment, scaffolding):
if experiment.logger is None:
root_logger = create_basic_stream_logger()
else:
root_logger = experiment.logger
for sc_path, scaffold in scaffolding.items():
if sc_path:
scaffold.logger = root_logger.getChild(sc_path)
else:
scaffold.logger = root_logger
return root_logger, root_logger.getChild(experiment.path)
def initialize_logging(experiment, scaffolding):
if experiment.logger is None:
root_logger = create_basic_stream_logger()
else:
root_logger = experiment.logger
for sc_path, scaffold in scaffolding.items():
if sc_path:
scaffold.logger = root_logger.getChild(sc_path)
else:
scaffold.logger = root_logger
return root_logger, root_logger.getChild(experiment.path)
def __init__(self,
experiment,
database_name=None,
url="localhost",
optimizer=None,
prefix='runs',
always_inject_observer=False):
"""
Create a new LabAssistant and connects it with a database.
Parameters
----------
experiment : sacred.Experiment
The (sacred) experiment that is going to be optimized.
database_name : str
The name of the database where all information about the runs
are saved.
optimizer: object, optional
Specifies which optimizer is used to suggest a new hyperparameter
configuration
prefix: str, optional
Additional prefix for the database
always_inject_observer: bool, optional
If true an MongoObserver is added to the experiment.
"""
self.ex = experiment
self.ex.option_hook(self._option_hook)
self.db_name = database_name
self.url = url
self.db = None
self.ex.logger = create_basic_stream_logger()
self.logger = self.ex.logger.getChild('LabAssistant')
self.prefix = prefix
self.version_policy = 'newer'
self.always_inject_observer = always_inject_observer
self.optimizer_class = optimizer
self.block_time = 1000 # TODO: what value should this be?
# remember for which experiments we have config hooks setup
self.observer_mapping = dict()
# mark that we have newer looked for finished runs
self.known_jobs = set()
self.last_checked = None
self.current_search_space = None
self.mongo_observer = None
def initialize_logging(experiment, scaffolding, loglevel=None):
if experiment.logger is None:
if loglevel:
try:
loglevel = int(loglevel)
except ValueError:
pass
root_logger = create_basic_stream_logger('', level=loglevel)
else:
root_logger = experiment.logger
if loglevel:
root_logger.setLevel(loglevel)
for sc_path, scaffold in scaffolding.items():
if sc_path:
scaffold.logger = root_logger.getChild(sc_path)
else:
scaffold.logger = root_logger
return root_logger.getChild(experiment.name)
def initialize_logging(experiment, scaffolding, log_level=None):
if experiment.logger is None:
root_logger = create_basic_stream_logger()
else:
root_logger = experiment.logger
for sc_path, scaffold in scaffolding.items():
if sc_path:
scaffold.logger = root_logger.getChild(sc_path)
else:
scaffold.logger = root_logger
# set log level
if log_level is not None:
try:
lvl = int(log_level)
except ValueError:
lvl = log_level
root_logger.setLevel(lvl)
return root_logger, root_logger.getChild(experiment.path)
def initialize_logging(experiment, scaffolding, log_level=None):
if experiment.logger is None:
root_logger = create_basic_stream_logger()
else:
root_logger = experiment.logger
for sc_path, scaffold in scaffolding.items():
if sc_path:
scaffold.logger = root_logger.getChild(sc_path)
else:
scaffold.logger = root_logger
# set log level
if log_level is not None:
try:
lvl = int(log_level)
except ValueError:
lvl = log_level
root_logger.setLevel(lvl)
return root_logger, root_logger.getChild(experiment.path)
