def train_config(request): config_path = request.param config = NestedNamespace() with open(config_path, "r") as f: defined_config = json.load(f) config.load_from_json(defined_config) config.nsml = NestedNamespace() config.nsml.pause = 0 config = optimize_config(config, is_test=True) set_gpu_env(config) config.data_reader.train_file_path = SQUAD_SYNTHETIC_DATA_PATH config.data_reader.valid_file_path = SQUAD_SYNTHETIC_DATA_PATH return config
help=""" NSML mode setting """, ) parser.add_argument( "--iteration", type=int, default=0, help=""" NSML default setting """, ) parser.add_argument( "--pause", type=int, default=0, help=""" NSML default setting """, ) args = parser.parse_args() with open(args.base_config, "r") as f: defined_config = json.load(f) config = NestedNamespace() config.load_from_json(defined_config) config.nsml = args set_logging_config() if args.mode == "train_and_evaluate": re_train_and_evaluate(config) elif args.mode == "test" or args.mode == "infer": test(config) else: raise ValueError(f"Unrecognized mode. {config.mode}")