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)