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}")