class Generator(object):
"""Convert search space and search algorithm, sample a new model."""
def __init__(self):
self.step_name = General.step_name
self.search_space = SearchSpace()
self.search_alg = SearchAlgorithm(self.search_space)
self.report = Report()
self.record = ReportRecord()
self.record.step_name = self.step_name
if hasattr(self.search_alg.config, 'objective_keys'):
self.record.objective_keys = self.search_alg.config.objective_keys
self.quota = QuotaCompare('restrict')
@property
def is_completed(self):
"""Define a property to determine search algorithm is completed."""
return self.search_alg.is_completed or self.quota.is_halted()
def sample(self):
"""Sample a work id and model from search algorithm."""
res = self.search_alg.search()
if not res:
return None
if not isinstance(res, list):
res = [res]
if len(res) == 0:
return None
out = []
for sample in res:
desc = sample.get("desc") if isinstance(sample, dict) else sample[1]
desc = self._decode_hps(desc)
model_desc = deepcopy(desc)
if "modules" in desc:
PipeStepConfig.model.model_desc = deepcopy(desc)
elif "network" in desc:
origin_desc = PipeStepConfig.model.model_desc
desc = update_dict(desc["network"], origin_desc)
PipeStepConfig.model.model_desc = deepcopy(desc)
if self.quota.is_filtered(desc):
continue
record = self.record.from_sample(sample, desc)
Report().broadcast(record)
out.append((record.worker_id, model_desc))
return out
def update(self, step_name, worker_id):
"""Update search algorithm accord to the worker path.
:param step_name: step name
:param worker_id: current worker id
:return:
"""
report = Report()
record = report.receive(step_name, worker_id)
logging.debug("Get Record=%s", str(record))
self.search_alg.update(record.serialize())
report.dump_report(record.step_name, record)
self.dump()
logging.info("Update Success. step_name=%s, worker_id=%s", step_name, worker_id)
logging.info("Best values: %s", Report().print_best(step_name=General.step_name))
@staticmethod
def _decode_hps(hps):
"""Decode hps: `trainer.optim.lr : 0.1` to dict format.
And convert to `zeus.common.config import Config` object
This Config will be override in Trainer or Datasets class
The override priority is: input hps > user configuration > default configuration
:param hps: hyper params
:return: dict
"""
hps_dict = {}
if hps is None:
return None
if isinstance(hps, tuple):
return hps
for hp_name, value in hps.items():
hp_dict = {}
for key in list(reversed(hp_name.split('.'))):
if hp_dict:
hp_dict = {key: hp_dict}
else:
hp_dict = {key: value}
# update cfg with hps
hps_dict = update_dict(hps_dict, hp_dict, [])
return Config(hps_dict)
def dump(self):
"""Dump generator to file."""
step_path = TaskOps().step_path
_file = os.path.join(step_path, ".generator")
with open(_file, "wb") as f:
pickle.dump(self, f, protocol=pickle.HIGHEST_PROTOCOL)
@classmethod
def restore(cls):
"""Restore generator from file."""
step_path = TaskOps().step_path
_file = os.path.join(step_path, ".generator")
if os.path.exists(_file):
with open(_file, "rb") as f:
return pickle.load(f)
else:
return None
class Generator(object):
"""Convert search space and search algorithm, sample a new model."""
def __init__(self):
self.step_name = General.step_name
self.search_space = SearchSpace()
self.search_alg = SearchAlgorithm(self.search_space)
if hasattr(self.search_alg.config, 'objective_keys'):
self.objective_keys = self.search_alg.config.objective_keys
self.quota = QuotaCompare('restrict')
self.affinity = None if General.quota.affinity.type is None else QuotaAffinity(
General.quota.affinity)
@property
def is_completed(self):
"""Define a property to determine search algorithm is completed."""
return self.search_alg.is_completed or self.quota.is_halted()
def sample(self):
"""Sample a work id and model from search algorithm."""
for _ in range(10):
res = self.search_alg.search()
if not res:
return None
if not isinstance(res, list):
res = [res]
if len(res) == 0:
return None
out = []
for sample in res:
if isinstance(sample, dict):
id = sample["worker_id"]
desc = self._decode_hps(sample["encoded_desc"])
sample.pop("worker_id")
sample.pop("encoded_desc")
kwargs = sample
sample = _split_sample((id, desc))
else:
kwargs = {}
sample = _split_sample(sample)
if hasattr(self, "objective_keys") and self.objective_keys:
kwargs["objective_keys"] = self.objective_keys
(id, desc, hps) = sample
if "modules" in desc:
PipeStepConfig.model.model_desc = deepcopy(desc)
elif "network" in desc:
origin_desc = PipeStepConfig.model.model_desc
model_desc = update_dict(desc["network"], origin_desc)
PipeStepConfig.model.model_desc = model_desc
desc.pop('network')
desc.update(model_desc)
if self.quota.is_filtered(desc):
continue
if self.affinity and not self.affinity.is_affinity(desc):
continue
ReportClient().update(General.step_name,
id,
desc=desc,
hps=hps,
**kwargs)
out.append((id, desc, hps))
if out:
break
return out
def update(self, step_name, worker_id):
"""Update search algorithm accord to the worker path.
:param step_name: step name
:param worker_id: current worker id
:return:
"""
record = ReportClient().get_record(step_name, worker_id)
logging.debug("Get Record=%s", str(record))
self.search_alg.update(record.serialize())
try:
self.dump()
except TypeError:
logging.warning(
"The Generator contains object which can't be pickled.")
logging.info(
f"Update Success. step_name={step_name}, worker_id={worker_id}")
logging.info("Best values: %s",
ReportServer().print_best(step_name=General.step_name))
@staticmethod
def _decode_hps(hps):
"""Decode hps: `trainer.optim.lr : 0.1` to dict format.
And convert to `vega.common.config import Config` object
This Config will be override in Trainer or Datasets class
The override priority is: input hps > user configuration > default configuration
:param hps: hyper params
:return: dict
"""
hps_dict = {}
if hps is None:
return None
if isinstance(hps, tuple):
return hps
for hp_name, value in hps.items():
hp_dict = {}
for key in list(reversed(hp_name.split('.'))):
if hp_dict:
hp_dict = {key: hp_dict}
else:
hp_dict = {key: value}
# update cfg with hps
hps_dict = update_dict(hps_dict, hp_dict, [])
return Config(hps_dict)
def dump(self):
"""Dump generator to file."""
step_path = TaskOps().step_path
_file = os.path.join(step_path, ".generator")
with open(_file, "wb") as f:
pickle.dump(self, f, protocol=pickle.HIGHEST_PROTOCOL)
@classmethod
def restore(cls):
"""Restore generator from file."""
step_path = TaskOps().step_path
_file = os.path.join(step_path, ".generator")
if os.path.exists(_file):
with open(_file, "rb") as f:
return pickle.load(f)
else:
return None