def _save_model(model_inst: BaseModel, proposal: Proposal, result: TrialResult,
param_cache: ParamCache, param_store: ParamStore):
if not proposal.to_cache_params and not proposal.to_save_params:
return None
print('Dumping model parameters...')
params = model_inst.dump_parameters()
if proposal.to_cache_params:
print('Storing shared params in cache...')
param_cache.store_params(params,
score=result.score,
time=datetime.now())
store_params_id = None
if proposal.to_save_params:
print('Saving shared params...')
store_params_id = param_store.save(params)
return store_params_id
class TrainWorker:
def __init__(self, service_id, worker_id):
self._worker_id = worker_id
self._monitor: _SubTrainJobMonitor = _SubTrainJobMonitor(service_id)
self._redis_host = os.environ['REDIS_HOST']
self._redis_port = os.environ['REDIS_PORT']
self._param_store: ParamStore = FileParamStore()
self._trial_id = None # ID of currently running trial
self._train_cache: TrainCache = None
self._param_cache: ParamCache = None
self._trial_errors = 0 # Consecutive traial errors
def start(self):
self._monitor.pull_job_info()
self._train_cache = TrainCache(self._monitor.sub_train_job_id,
self._redis_host, self._redis_port)
self._param_cache = ParamCache(self._monitor.sub_train_job_id,
self._redis_host, self._redis_port)
logger.info(
f'Starting worker for sub train job "{self._monitor.sub_train_job_id}"...'
)
self._notify_start()
while True:
proposal = self._fetch_proposal()
if proposal is not None:
result = self._perform_trial(proposal)
self._submit_result(result)
time.sleep(LOOP_SLEEP_SECS)
def stop(self):
self._notify_stop()
# If worker is currently running a trial, mark it has errored
try:
if self._trial_id is not None:
self._monitor.mark_trial_as_errored(self._trial_id)
except:
logger.error('Error marking trial as errored:')
logger.error(traceback.format_exc())
# Run model class teardown
try:
self._monitor.model_class.teardown()
except:
logger.error('Error tearing down model class:')
logger.error(traceback.format_exc())
def _notify_start(self):
superadmin_client().send_event(
'train_job_worker_started',
sub_train_job_id=self._monitor.sub_train_job_id)
self._train_cache.add_worker(self._worker_id)
def _fetch_proposal(self):
proposal = self._train_cache.get_proposal(self._worker_id)
return proposal
def _perform_trial(self, proposal: Proposal) -> TrialResult:
self._trial_id = proposal.trial_id
logger.info(
f'Starting trial {self._trial_id} with proposal {proposal}...')
try:
# Setup logging
logger_info = self._start_logging_to_trial(
lambda log_line, log_lvl: self._monitor.log_to_trial(
self._trial_id, log_line, log_lvl))
self._monitor.mark_trial_as_running(self._trial_id, proposal)
shared_params = self._pull_shared_params(proposal)
model_inst = self._load_model(proposal)
self._train_model(model_inst, proposal, shared_params)
result = self._evaluate_model(model_inst, proposal)
store_params_id = self._save_model(model_inst, proposal, result)
model_inst.destroy()
self._monitor.mark_trial_as_completed(self._trial_id, result.score,
store_params_id)
self._trial_errors = 0
return result
except Exception as e:
logger.error('Error while running trial:')
logger.error(traceback.format_exc())
self._monitor.mark_trial_as_errored(self._trial_id)
# Ensure that trial doesn't error too many times consecutively
self._trial_errors += 1
if self._trial_errors > MAX_CONSEC_TRIAL_ERRORS:
logger.error(
f'Reached {MAX_CONSEC_TRIAL_ERRORS} consecutive errors - raising exception'
)
raise e
return TrialResult(proposal)
finally:
self._stop_logging_to_trial(logger_info)
# Untie from done trial
self._trial_id = None
def _notify_stop(self):
self._train_cache.delete_worker(self._worker_id)
superadmin_client().send_event(
'train_job_worker_stopped',
sub_train_job_id=self._monitor.sub_train_job_id)
def _start_logging_to_trial(self, handle_log):
# Add log handlers for trial, including adding handler to root logger
# to capture any logs emitted with level above INFO during model training & evaluation
log_handler = LoggerUtilsHandler(handle_log)
py_model_logger = logging.getLogger('{}.trial'.format(__name__))
py_model_logger.setLevel(logging.INFO)
py_model_logger.propagate = False # Avoid duplicate logs in root logger
py_model_logger.addHandler(log_handler)
model_logger.set_logger(py_model_logger)
root_logger = logging.getLogger()
root_logger.addHandler(log_handler)
return (root_logger, py_model_logger, log_handler)
def _load_model(self, proposal: Proposal):
logger.info('Creating model instance...')
py_model_class = self._monitor.model_class
model_inst = py_model_class(**proposal.knobs)
return model_inst
def _pull_shared_params(self, proposal: Proposal):
if proposal.params_type == ParamsType.NONE:
return None
logger.info('Retrieving shared params from cache...')
shared_params = self._param_cache.retrieve_params(proposal.params_type)
return shared_params
def _train_model(self, model_inst: BaseModel, proposal: Proposal,
shared_params: Union[dict, None]):
train_dataset_path = self._monitor.train_dataset_path
train_args = self._monitor.train_args
logger.info('Training model...')
model_inst.train(train_dataset_path,
shared_params=shared_params,
**(train_args or {}))
def _evaluate_model(self, model_inst: BaseModel,
proposal: Proposal) -> TrialResult:
val_dataset_path = self._monitor.val_dataset_path
if not proposal.to_eval:
return TrialResult(proposal)
logger.info('Evaluating model...')
score = model_inst.evaluate(val_dataset_path)
logger.info(f'Score on validation dataset: {score}')
return TrialResult(proposal, score=score)
def _save_model(self, model_inst: BaseModel, proposal: Proposal,
result: TrialResult):
if not proposal.to_cache_params and not proposal.to_save_params:
return None
logger.info('Dumping model parameters...')
params = model_inst.dump_parameters()
if proposal.to_cache_params:
logger.info('Storing shared params in cache...')
self._param_cache.store_params(params,
score=result.score,
time=datetime.now())
store_params_id = None
if proposal.to_save_params:
logger.info('Saving shared params...')
store_params_id = self._param_store.save(params)
return store_params_id
def _submit_result(self, result: TrialResult):
self._train_cache.create_result(self._worker_id, result)
self._train_cache.delete_proposal(self._worker_id)
def _stop_logging_to_trial(self, logger_info):
(root_logger, py_model_logger, log_handler) = logger_info
# Remove log handlers from loggers for this trial
root_logger.removeHandler(log_handler)
py_model_logger.removeHandler(log_handler)