def main(hparams):
"""
Main training routine specific for this project
:param hparams:
"""
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel(hparams)
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = pl.Trainer(
max_epochs=hparams.epochs,
gpus=hparams.gpus,
distributed_backend=hparams.distributed_backend,
precision=16 if hparams.use_16bit else 32,
)
# ------------------------
# 3 START TRAINING
# ------------------------
# trainer.fit(model)
trainer.test()
def run_cli():
root_dir = os.path.dirname(os.path.realpath(__file__))
parent_parser = ArgumentParser(add_help=False)
# each LightningModule defines arguments relevant to it
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
# ---------------------
# RUN TRAINING
# ---------------------
main(args)
def main(args):
""" Main training routine specific for this project. """
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel(**vars(args))
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = Trainer.from_argparse_args(args)
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
def main(hparams):
"""Main training routine specific for this project."""
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel(hparams)
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = pl.Trainer(gpus=2, num_nodes=2, distributed_backend='ddp')
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
def run_cli():
# ------------------------
# TRAINING ARGUMENTS
# ------------------------
# these are project-wide arguments
root_dir = os.path.dirname(os.path.realpath(__file__))
parent_parser = ArgumentParser(add_help=False)
# each LightningModule defines arguments relevant to it
parser = LightningTemplateModel.add_model_specific_args(
parent_parser, root_dir)
parser = Trainer.add_argparse_args(parser)
parser.set_defaults(gpus=2)
args = parser.parse_args()
# ---------------------
# RUN TRAINING
# ---------------------
main(args)
def main(hparams):
"""
Main training routine specific for this project
:param hparams:
"""
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel(hparams)
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = pl.Trainer(max_epochs=hparams.epochs)
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
def main(hparams):
"""
Main training routine specific for this project
:param hparams:
"""
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel(hparams)
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = pl.Trainer(max_epochs=hparams.epochs,
overfit_pct=0.01,
early_stop_callback=True)
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
def main(args):
"""Main training routine specific for this project."""
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel()
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = Trainer(
gpus=args.gpus,
num_nodes=args.num_nodes,
distributed_backend='ddp',
max_epochs=args.max_epochs,
max_steps=args.max_steps,
)
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = pl.Trainer.from_argparse_args(args)
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
if __name__ == '__main__':
# ------------------------
# TRAINING ARGUMENTS
# ------------------------
# these are project-wide arguments
root_dir = os.path.dirname(os.path.realpath(__file__))
parent_parser = ArgumentParser(add_help=False)
# each LightningModule defines arguments relevant to it
parser = LightningTemplateModel.add_model_specific_args(
parent_parser, root_dir)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# ---------------------
# RUN TRAINING
# ---------------------
main(args)
