def start_training(bot: str, user: str, token: str = None, reload=True): """ prevents training of the bot, if the training session is in progress otherwise start training :param reload: whether to reload model in the cache :param bot: bot id :param token: JWT token for remote model reload :param user: user id :return: model path """ exception = None model_file = None training_status = None if Utility.environment.get('model') and Utility.environment['model'][ 'train'].get('event_url'): Utility.train_model_event(bot, user, token) else: try: model_file = train_model_for_bot(bot) training_status = MODEL_TRAINING_STATUS.DONE.value agent_url = Utility.environment['model']['train'].get('agent_url') if agent_url: Utility.http_request( 'get', urljoin(agent_url, "/api/bot/model/reload"), token, user) else: if reload: AgentProcessor.reload(bot) except Exception as e: logging.exception(e) training_status = MODEL_TRAINING_STATUS.FAIL.value exception = str(e) finally: ModelProcessor.set_training_status( bot=bot, user=user, status=training_status, model_path=model_file, exception=exception, ) return model_file
def start_training(bot: str, user: str, reload=True): """ prevents training of the bot, if the training session is in progress otherwise start training :param reload: whether to reload model in the cache :param bot: bot id :param user: user id :return: model path """ exception = None model_file = None training_status = None ModelProcessor.set_training_status( bot=bot, user=user, status=MODEL_TRAINING_STATUS.INPROGRESS.value, ) try: model_file = train_model_for_bot(bot) training_status = MODEL_TRAINING_STATUS.DONE.value except Exception as e: logging.exception(e) training_status = MODEL_TRAINING_STATUS.FAIL.value exception = str(e) raise AppException(exception) finally: ModelProcessor.set_training_status( bot=bot, user=user, status=training_status, model_path=model_file, exception=exception, ) if reload: AgentProcessor.reload(bot) return model_file