def model_start(model_name):
global g_storage
params = {
'save_prediction': get_bool_arg('save_prediction'),
'datasink': request.args.get('datasink'),
'detect_anomalies': get_bool_arg('detect_anomalies'),
}
model = g_storage.load_model(model_name)
if not model.is_trained:
raise errors.ModelNotTrained()
model.set_run_params(params)
model.set_run_state(None)
g_storage.save_model(model)
params['from_date'] = get_date_arg('from')
try:
_model_start(model, params)
except errors.LoudMLException as exn:
model.set_run_params(None)
g_storage.save_model(model)
raise (exn)
return "real-time prediction started", 200
def _done_cb(self, result):
"""
Callback executed when job is done
"""
super()._done_cb(result)
if self.state == 'done' and self.autostart:
logging.info("scheduling autostart for model '%s'",
self.model_name)
model = g_storage.load_model(self.model_name)
params = self._kwargs_start.copy()
params.pop('from_date')
model.set_run_params(params)
g_storage.save_model(model)
try:
_model_start(model, self._kwargs_start)
except errors.LoudMLException:
model.set_run_params(None)
g_storage.save_model(model)
def model_stop(model_name):
global g_running_models
global g_storage
g_lock.acquire()
timer = g_running_models.get(model_name)
if timer is None:
g_lock.release()
return "model is not active", 404
timer.cancel()
del g_running_models[model_name]
g_lock.release()
logging.info("model '%s' deactivated", model_name)
model = g_storage.load_model(model_name)
model.set_run_params(None)
model.set_run_state(None)
g_storage.save_model(model)
return "model deactivated"
