def train_translation_model(data_dir,
arch,
extra_flags=None,
task='translation',
run_validation=False,
lang_flags=None,
extra_valid_flags=None):
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',
'1',
'--num-workers',
'0',
] + lang_flags + (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',
'--num-workers',
'0',
] + lang_flags + (extra_valid_flags or []))
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_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)
