class MTBottleneckConfig(NemoConfig):
name: Optional[str] = 'MTBottleneck'
do_training: bool = True
do_testing: bool = False
model: MTBottleneckModelConfig = MTBottleneckModelConfig()
trainer: Optional[TrainerConfig] = TrainerConfig()
exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(name='MTBottleneck', files_to_copy=[])
def setup_model(self):
# Stateless timer for 3 seconds.
# Max steps shouldn't matter for it should stop in 3 seconds based on the timer.
# Val check interval makes sure a checkpoint is written and can be restored from.
callback_params = CallbackParams()
callback_params.monitor = "val_loss"
callback_params.save_top_k = 1
trainer = Trainer(
devices=1,
val_check_interval=5,
max_steps=10000,
accelerator='gpu',
strategy='ddp',
logger=None,
callbacks=[StatelessTimer('00:00:00:03')],
checkpoint_callback=False,
)
exp_manager_cfg = ExpManagerConfig(
explicit_log_dir='./ptl_stateless_timer_check/',
use_datetime_version=False,
version="",
resume_ignore_no_checkpoint=True,
create_checkpoint_callback=True,
checkpoint_callback_params=callback_params,
resume_if_exists=True,
)
exp_manager(trainer, cfg=OmegaConf.structured(exp_manager_cfg))
model = ExampleModel(trainer=trainer)
trainer.fit(model)
return trainer
class PunctuationCapitalizationConfig(NemoConfig):
"""
A config for punctuation model training and testing.
See an example of full config in
`nemo/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml
<https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml>`_
"""
pretrained_model: Optional[str] = None
"""Can be an NVIDIA's NGC cloud model or a path to a .nemo checkpoint. You can get list of possible cloud options
by calling method
:func:`~nemo.collections.nlp.models.token_classification.punctuation_capitalization_model.PunctuationCapitalizationModel.list_available_models`.
"""
name: Optional[str] = 'Punctuation_and_Capitalization'
"""A name of the model. Used for naming output directories and ``.nemo`` checkpoints."""
do_training: bool = True
"""Whether to perform training of the model."""
do_testing: bool = False
"""Whether ot perform testing of the model."""
model: PunctuationCapitalizationModelConfig = PunctuationCapitalizationModelConfig()
"""A configuration for the
:class:`~nemo.collections.nlp.models.token_classification.punctuation_capitalization_model.PunctuationCapitalizationModel`
model."""
trainer: Optional[TrainerConfig] = TrainerConfig()
"""Contains ``Trainer`` Lightning class constructor parameters."""
exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(name=name, files_to_copy=[])
"""A configuration with various NeMo training options such as output directories, resuming from checkpoint,
def instantiate_multinode_ddp_if_possible():
num_gpus = torch.cuda.device_count()
trainer = Trainer(gpus=num_gpus,
accelerator='ddp',
logger=None,
checkpoint_callback=None)
exp_manager_cfg = ExpManagerConfig(exp_dir='./ddp_check/',
use_datetime_version=False,
version="")
exp_manager(trainer, cfg=OmegaConf.structured(exp_manager_cfg))
return trainer
class MTEncDecConfig(NemoConfig):
model: AAYNBaseConfig = AAYNBaseConfig()
trainer: Optional[TrainerConfig] = TrainerConfig()
exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(
name='MTEncDec', files_to_copy=[])
class MTEncDecConfig(NemoConfig):
name: Optional[str] = 'MTEncDec'
do_training: bool = True
model: MTEncDecModelConfig = MTEncDecModelConfig()
trainer: Optional[TrainerConfig] = TrainerConfig()
exp_manager: Optional[ExpManagerConfig] = ExpManagerConfig(name='MTEncDec', files_to_copy=[])