def cli_main():
parser = options.get_validation_parser()
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = options.get_validation_parser()
override_args = options.parse_args_and_arch(override_parser,
suppress_defaults=True)
main(args, override_args)
def cli_main():
parser = options.get_validation_parser()
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = options.get_validation_parser()
override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True)
distributed_utils.call_main(convert_namespace_to_omegaconf(args), main, override_args=override_args)
def cli_main():
parser = options.get_validation_parser()
add_distributed_training_args(parser)
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = options.get_validation_parser()
add_distributed_training_args(override_parser)
override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True)
distributed_utils.call_main(args, main, override_args=override_args)
def cli_main():
parser = options.get_validation_parser()
parser.add_argument(
'--config',
type=str,
nargs='*',
help=
'paths to JSON files of experiment configurations, from high to low priority'
)
parser.add_argument(
'--load-checkpoint',
type=str,
help='path to checkpoint to load (possibly composite) model from')
pre_parsed_args = parser.parse_args()
config_dict = {}
for config_path in pre_parsed_args.config:
config_dict = update_config(config_dict, compose_configs(config_path))
parser_modifier = modify_factory(config_dict)
args = options.parse_args_and_arch(parser, modify_parser=parser_modifier)
update_namespace(args, config_dict)
main(args)
def train_translation_model(data_dir, arch, extra_flags=None, task='translation'):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', task,
data_dir,
'--save-dir', data_dir,
'--arch', arch,
'--lr', '0.05',
'--max-tokens', '500',
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
'--source-lang', 'in',
'--target-lang', 'out',
] + (extra_flags or []),
)
train.main(train_args)
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
'--task', task,
data_dir,
'--path', os.path.join(data_dir, 'checkpoint_last.pt'),
'--valid-subset', 'valid',
'--max-tokens', '500',
'--no-progress-bar',
]
)
validate.main(validate_args)
def cli_main():
parser = options.get_validation_parser()
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = options.get_validation_parser()
override_args = options.parse_args_and_arch(override_parser,
suppress_defaults=True)
# support multi-gpu validation, use all available gpus
default_world_size = max(1, torch.cuda.device_count())
if args.distributed_world_size < default_world_size:
args.distributed_world_size = default_world_size
override_args.distributed_world_size = default_world_size
distributed_utils.call_main(args, main, override_args=override_args)
def train_language_model(data_dir,
arch,
extra_flags=None,
run_validation=False):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
] + (extra_flags or []),
)
train.main(train_args)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
"language_modeling",
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
],
)
validate.main(validate_args)
def train_language_model(data_dir,
arch,
extra_flags=None,
run_validation=False):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task',
'language_modeling',
data_dir,
'--arch',
arch,
'--optimizer',
'adam',
'--lr',
'0.0001',
'--criterion',
'adaptive_loss',
'--adaptive-softmax-cutoff',
'5,10,15',
'--max-tokens',
'500',
'--tokens-per-sample',
'500',
'--save-dir',
data_dir,
'--max-epoch',
'1',
'--no-progress-bar',
'--distributed-world-size',
'1',
'--ddp-backend',
'no_c10d',
] + (extra_flags or []),
)
train.main(train_args)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(validate_parser, [
'--task',
'language_modeling',
data_dir,
'--path',
os.path.join(data_dir, 'checkpoint_last.pt'),
'--valid-subset',
'valid',
'--max-tokens',
'500',
'--no-progress-bar',
])
validate.main(validate_args)
def train_translation_model(data_dir,
arch,
extra_flags=None,
task="translation",
run_validation=False):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--save-dir",
data_dir,
"--arch",
arch,
"--lr",
"0.05",
"--max-tokens",
"500",
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--source-lang",
"in",
"--target-lang",
"out",
] + (extra_flags or []),
)
train.main(train_args)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
],
)
validate.main(validate_args)
def cli_main():
parser = options.get_validation_mul_parser()
args = options.parse_args_and_arch(parser)
override_parser = options.get_validation_parser()
override_args = options.parse_args_and_arch(override_parser,
suppress_defaults=True)
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, start_rank),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
assert args.distributed_world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
args.distributed_init_method = 'tcp://localhost:{port}'.format(
port=port)
args.distributed_rank = None # set based on device id
#if max(args.update_freq) > 1 and args.ddp_backend != 'no_c10d':
# print('| NOTE: you may get better performance with: --ddp-backend=no_c10d')
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, override_args),
nprocs=args.distributed_world_size,
)
else:
# single GPU training
main(args, override_args)
def train_language_model(
data_dir,
arch,
extra_flags=None,
run_validation=False,
extra_valid_flags=None,
task="language_modeling",
world_size=1,
):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
] + (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
] + (extra_valid_flags or []),
)
validate.main(validate_args)
def train_translation_model(
data_dir,
arch,
extra_flags=None,
task="translation",
run_validation=False,
lang_flags=None,
extra_valid_flags=None,
world_size=1,
):
if lang_flags is None:
lang_flags = [
"--source-lang",
"in",
"--target-lang",
"out",
]
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--save-dir",
data_dir,
"--arch",
arch,
"--optimizer",
"nag",
"--lr",
"0.05",
"--max-tokens",
"500",
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--num-workers",
"0",
] + lang_flags + (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
] + lang_flags + (extra_valid_flags or []),
)
validate.main(validate_args)