examples/research_projects/seq2seq-distillation/lightning_base.py/generic_train
def generic_train(
    model: BaseTransformer,
    args: argparse.Namespace,
    early_stopping_callback=None,
    logger=True,  # can pass WandbLogger() here
    extra_callbacks=[],
    checkpoint_callback=None,
    logging_callback=None,
    **extra_train_kwargs
):
    pl.seed_everything(args.seed)

    # init model
    odir = Path(model.hparams.output_dir)
    odir.mkdir(exist_ok=True)

    # add custom checkpoints
    if checkpoint_callback is None:
        checkpoint_callback = pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
        )
    if early_stopping_callback:
        extra_callbacks.append(early_stopping_callback)
    if logging_callback is None:
        logging_callback = LoggingCallback()

    train_params = {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fp16:
        train_params["precision"] = 16
        train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        train_params["distributed_backend"] = "ddp"

    train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
    train_params["accelerator"] = extra_train_kwargs.get("accelerator", None)
    train_params["profiler"] = extra_train_kwargs.get("profiler", None)

    trainer = pl.Trainer.from_argparse_args(
        args,
        weights_summary=None,
        callbacks=[logging_callback] + extra_callbacks,
        logger=logger,
        checkpoint_callback=checkpoint_callback,
        **train_params,
    )

    if args.do_train:
        trainer.fit(model)

    return trainer