def main(args): utils.configure_colored_logging(args.loglevel) pre_load = args.pre_load _endpoints = read_endpoints(args.endpoints) router = DataRouter( args.path, args.max_training_processes, args.response_log, args.emulate, args.storage, model_server=_endpoints.model, wait_time_between_pulls=args.wait_time_between_pulls ) if pre_load: logger.debug('Preloading....') if 'all' in pre_load: pre_load = router.project_store.keys() router._pre_load(pre_load) rasa = RasaNLU( router, args.loglevel, args.write, args.num_threads, get_token(cmdline_args.token), args.cors, default_config_path=args.config ) logger.info('Started http server on port %s' % args.port) rasa.app.run('0.0.0.0', args.port)
def main(args): utils.configure_colored_logging(args.loglevel) pre_load = args.pre_load _endpoints = read_endpoints(args.endpoints) router = DataRouter(args.path, args.max_training_processes, args.response_log, args.emulate, args.storage, model_server=_endpoints.model, wait_time_between_pulls=args.wait_time_between_pulls) if pre_load: logger.debug('Preloading....') if 'all' in pre_load: pre_load = router.project_store.keys() router._pre_load(pre_load) rasa = RasaNLU(router, args.loglevel, args.write, args.num_threads, get_token(cmdline_args.token), args.cors, default_config_path=args.config) logger.info('Started http server on port %s' % args.port) rasa.app.run('0.0.0.0', args.port)
def main(): parser = create_argument_parser() cmdline_args = parser.parse_args() utils.configure_colored_logging(cmdline_args.loglevel) if cmdline_args.mode == "crossvalidation": # TODO: move parsing into sub parser # manual check argument dependency if cmdline_args.model is not None: parser.error("Crossvalidation will train a new model " "- do not specify external model.") if cmdline_args.config is None: parser.error("Crossvalidation will train a new model " "you need to specify a model configuration.") nlu_config = config.load(cmdline_args.config) data = training_data.load_data(cmdline_args.data) data = drop_intents_below_freq(data, cutoff=5) results, entity_results = run_cv_evaluation( data, int(cmdline_args.folds), nlu_config) logger.info("CV evaluation (n={})".format(cmdline_args.folds)) if any(results): logger.info("Intent evaluation results") return_results(results.train, "train") return_results(results.test, "test") if any(entity_results): logger.info("Entity evaluation results") return_entity_results(entity_results.train, "train") return_entity_results(entity_results.test, "test") elif cmdline_args.mode == "evaluation": run_evaluation(cmdline_args.data, cmdline_args.model, cmdline_args.report, cmdline_args.successes, cmdline_args.errors, cmdline_args.confmat, cmdline_args.histogram) logger.info("Finished evaluation")
def main(): parser = create_argument_parser() cmdline_args = parser.parse_args() utils.configure_colored_logging(cmdline_args.loglevel) if cmdline_args.mode == "crossvalidation": # TODO: move parsing into sub parser # manual check argument dependency if cmdline_args.model is not None: parser.error("Crossvalidation will train a new model " "- do not specify external model.") if cmdline_args.config is None: parser.error("Crossvalidation will train a new model " "you need to specify a model configuration.") nlu_config = config.load(cmdline_args.config) data = training_data.load_data(cmdline_args.data) data = drop_intents_below_freq(data, cutoff=5) results, entity_results = cross_validate( data, int(cmdline_args.folds), nlu_config) logger.info("CV evaluation (n={})".format(cmdline_args.folds)) if any(results): logger.info("Intent evaluation results") return_results(results.train, "train") return_results(results.test, "test") if any(entity_results): logger.info("Entity evaluation results") return_entity_results(entity_results.train, "train") return_entity_results(entity_results.test, "test") elif cmdline_args.mode == "evaluation": run_evaluation(cmdline_args.data, cmdline_args.model, cmdline_args.report, cmdline_args.successes, cmdline_args.errors, cmdline_args.confmat, cmdline_args.histogram) logger.info("Finished evaluation")
params.get('project', RasaNLUModelConfig.DEFAULT_PROJECT_NAME), params.get('model') ) return simplejson.dumps(response) except Exception as e: request.setResponseCode(500) logger.exception(e) return simplejson.dumps({"error": "{}".format(e)}) if __name__ == '__main__': # Running as standalone python application cmdline_args = create_argument_parser().parse_args() utils.configure_colored_logging(cmdline_args.loglevel) pre_load = cmdline_args.pre_load _endpoints = read_endpoints(cmdline_args.endpoints) router = DataRouter( cmdline_args.path, cmdline_args.max_training_processes, cmdline_args.response_log, cmdline_args.emulate, cmdline_args.storage, model_server=_endpoints.model, wait_time_between_pulls=cmdline_args.wait_time_between_pulls ) if pre_load: logger.debug('Preloading....')
response = self.data_router.unload_model( params.get('project', RasaNLUModelConfig.DEFAULT_PROJECT_NAME), params.get('model') ) return simplejson.dumps(response) except Exception as e: request.setResponseCode(500) logger.exception(e) return simplejson.dumps({"error": "{}".format(e)}) if __name__ == '__main__': # Running as standalone python application cmdline_args = create_argument_parser().parse_args() utils.configure_colored_logging(cmdline_args.loglevel) router = DataRouter(cmdline_args.path, cmdline_args.max_training_processes, cmdline_args.response_log) rasa = RasaNLU( router, cmdline_args.loglevel, cmdline_args.write, cmdline_args.num_threads, cmdline_args.token, cmdline_args.cors, default_config_path=cmdline_args.config ) logger.info('Started http server on port %s' % cmdline_args.port)