examples/research_projects/rag-end2end-retriever/finetune_rag.py/main
def main(args=None, model=None) -> GenerativeQAModule:
    parser = argparse.ArgumentParser()
    parser = pl.Trainer.add_argparse_args(parser)
    parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
    parser = GenerativeQAModule.add_retriever_specific_args(parser)
    args = args or parser.parse_args()

    Path(args.output_dir).mkdir(exist_ok=True)
    Path(args.output_dir + "/dpr_ctx_checkpoint").mkdir(
        exist_ok=True
    )  # save dpr_context encoder seprately for the future use
    print(args.shard_dir)
    if os.path.exists(args.shard_dir):  # we do not need previous kb shards used in dataset re-conding and re-indexing
        shutil.rmtree(args.shard_dir)
    Path(args.shard_dir).mkdir(exist_ok=True)

    if os.path.exists(
        args.cache_dir
    ):  # we do not need previous cache files used in dataset re-conding and re-indexing
        shutil.rmtree(args.cache_dir)
    Path(args.cache_dir).mkdir(exist_ok=True)

    named_actors = []
    if args.distributed_retriever == "ray" and args.gpus > 1:
        if not is_ray_available():
            raise RuntimeError("Please install Ray to use the Ray " "distributed retriever.")
        # Connect to an existing Ray cluster.
        try:
            ray.init(address=args.ray_address)
        except (ConnectionError, ValueError):
            logger.warning(
                "Connection to Ray cluster failed. Make sure a Ray"
                "cluster is running by either using Ray's cluster "
                "launcher (`ray up`) or by manually starting Ray on "
                "each node via `ray start --head` for the head node "
                "and `ray start --address='<ip address>:6379'` for "
                "additional nodes. See "
                "https://docs.ray.io/en/master/cluster/index.html "
                "for more info."
            )
            raise

        # Create Ray actors only for rank 0.
        if ("LOCAL_RANK" not in os.environ or os.environ["LOCAL_RANK"] == 0) and (
            "NODE_RANK" not in os.environ or os.environ["NODE_RANK"] == 0
        ):
            remote_cls = ray.remote(RayRetriever)
            named_actors = [
                remote_cls.options(name="retrieval_worker_{}".format(i)).remote()
                for i in range(args.num_retrieval_workers)
            ]
        else:
            logger.info(
                "Getting named actors for NODE_RANK {}, LOCAL_RANK {}".format(
                    os.environ["NODE_RANK"], os.environ["LOCAL_RANK"]
                )
            )
            named_actors = [ray.get_actor("retrieval_worker_{}".format(i)) for i in range(args.num_retrieval_workers)]
    args.actor_handles = named_actors
    assert args.actor_handles == named_actors

    if model is None:
        model: GenerativeQAModule = GenerativeQAModule(args)

    dataset = Path(args.data_dir).name
    if (
        args.logger_name == "default"
        or args.fast_dev_run
        or str(args.output_dir).startswith("/tmp")
        or str(args.output_dir).startswith("/var")
    ):
        training_logger = True  # don't pollute wandb logs unnecessarily
    elif args.logger_name == "wandb":
        from pytorch_lightning.loggers import WandbLogger

        project = os.environ.get("WANDB_PROJECT", dataset)
        training_logger = WandbLogger(name=model.output_dir.name, project=project)

    elif args.logger_name == "wandb_shared":
        from pytorch_lightning.loggers import WandbLogger

        training_logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}")

    es_callback = (
        get_early_stopping_callback(model.val_metric, args.early_stopping_patience)
        if args.early_stopping_patience >= 0
        else False
    )

    trainer: pl.Trainer = generic_train(
        model,
        args,
        logging_callback=Seq2SeqLoggingCallback(),
        checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
        early_stopping_callback=es_callback,
        logger=training_logger,
        profiler=pl.profiler.AdvancedProfiler() if args.profile else None,
    )

    pickle_save(model.hparams, model.output_dir / "hparams.pkl")
    if not args.do_predict:
        return model

    # test() without a model tests using the best checkpoint automatically
    trainer.test()
    return model