fastmri_examples/unet/train_unet_demo.py [136:190]:
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        lr=0.001,  # RMSProp learning rate
        lr_step_size=40,  # epoch at which to decrease learning rate
        lr_gamma=0.1,  # extent to which to decrease learning rate
        weight_decay=0.0,  # weight decay regularization strength
    )

    # trainer config
    parser = pl.Trainer.add_argparse_args(parser)
    parser.set_defaults(
        gpus=num_gpus,  # number of gpus to use
        replace_sampler_ddp=False,  # this is necessary for volume dispatch during val
        accelerator=backend,  # what distributed version to use
        seed=42,  # random seed
        deterministic=True,  # makes things slower, but deterministic
        default_root_dir=default_root_dir,  # directory for logs and checkpoints
        max_epochs=50,  # max number of epochs
    )

    args = parser.parse_args()

    # configure checkpointing in checkpoint_dir
    checkpoint_dir = args.default_root_dir / "checkpoints"
    if not checkpoint_dir.exists():
        checkpoint_dir.mkdir(parents=True)

    args.callbacks = [
        pl.callbacks.ModelCheckpoint(
            dirpath=args.default_root_dir / "checkpoints",
            save_top_k=True,
            verbose=True,
            monitor="validation_loss",
            mode="min",
        )
    ]

    # set default checkpoint if one exists in our checkpoint directory
    if args.resume_from_checkpoint is None:
        ckpt_list = sorted(checkpoint_dir.glob("*.ckpt"), key=os.path.getmtime)
        if ckpt_list:
            args.resume_from_checkpoint = str(ckpt_list[-1])

    return args


def run_cli():
    args = build_args()

    # ---------------------
    # RUN TRAINING
    # ---------------------
    cli_main(args)


if __name__ == "__main__":
    run_cli()
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fastmri_examples/varnet/train_varnet_demo.py [142:196]:
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        lr=0.001,  # Adam learning rate
        lr_step_size=40,  # epoch at which to decrease learning rate
        lr_gamma=0.1,  # extent to which to decrease learning rate
        weight_decay=0.0,  # weight regularization strength
    )

    # trainer config
    parser = pl.Trainer.add_argparse_args(parser)
    parser.set_defaults(
        gpus=num_gpus,  # number of gpus to use
        replace_sampler_ddp=False,  # this is necessary for volume dispatch during val
        accelerator=backend,  # what distributed version to use
        seed=42,  # random seed
        deterministic=True,  # makes things slower, but deterministic
        default_root_dir=default_root_dir,  # directory for logs and checkpoints
        max_epochs=50,  # max number of epochs
    )

    args = parser.parse_args()

    # configure checkpointing in checkpoint_dir
    checkpoint_dir = args.default_root_dir / "checkpoints"
    if not checkpoint_dir.exists():
        checkpoint_dir.mkdir(parents=True)

    args.callbacks = [
        pl.callbacks.ModelCheckpoint(
            dirpath=args.default_root_dir / "checkpoints",
            save_top_k=True,
            verbose=True,
            monitor="validation_loss",
            mode="min",
        )
    ]

    # set default checkpoint if one exists in our checkpoint directory
    if args.resume_from_checkpoint is None:
        ckpt_list = sorted(checkpoint_dir.glob("*.ckpt"), key=os.path.getmtime)
        if ckpt_list:
            args.resume_from_checkpoint = str(ckpt_list[-1])

    return args


def run_cli():
    args = build_args()

    # ---------------------
    # RUN TRAINING
    # ---------------------
    cli_main(args)


if __name__ == "__main__":
    run_cli()
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