def METHOD_NAME(cfg) -> None:
    logging.info("\n\n************** Experiment configuration ***********")
    logging.info(f'\n{OmegaConf.to_yaml(cfg)}')
    ###### following is the workaround for num_workers=0 issue #####
    # import torch.multiprocessing as mp
    # mp.set_start_method("spawn", force=True)
    #####################################################
    megatron_amp_o2 = cfg.model.get('megatron_amp_O2', False)
    plugins = []
    strategy = NLPDDPStrategy(
        no_ddp_communication_hook=True if megatron_amp_o2 else False,
        gradient_as_bucket_view=cfg.model.gradient_as_bucket_view,
        find_unused_parameters=False,
        timeout=datetime.timedelta(seconds=18000),
    )
    if cfg.trainer.precision in [16, '16', '16-mixed', 'bf16', 'bf16-mixed']:
        scaler = None
        if cfg.trainer.precision in [16, '16', '16-mixed']:
            scaler = GradScaler(
                init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32),
                growth_interval=cfg.model.get('native_amp_growth_interval', 1000),
                hysteresis=cfg.model.get('hysteresis', 2),
            )
            # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed
            plugin_precision = '16-mixed'
        else:
            plugin_precision = 'bf16-mixed'
        if megatron_amp_o2:
            plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler))
        else:
            plugins.append(MixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler))
    if cfg.get('cluster_type', None) == 'BCP':
        plugins.append(TorchElasticEnvironment())
    trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=[CustomProgressBar()])
    exp_manager(trainer, cfg.exp_manager)
    logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}')
    # Override timer callback to a stateless one
    for idx, callback in enumerate(trainer.callbacks):
        if isinstance(callback, Timer):
            trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time,)
    # load existing or init new soft prompt GPT model
    if cfg.model.get("restore_path", None):
        save_restore_connector = NLPSaveRestoreConnector()
        if os.path.isdir(cfg.model.restore_path):
            save_restore_connector.model_extracted_dir = cfg.model.restore_path
        model_cfg = MegatronRetroFinetuneModel.restore_from(
            restore_path=cfg.model.restore_path,
            trainer=trainer,
            return_config=True,
            save_restore_connector=save_restore_connector,
        )
        # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams
        model = load_from_nemo(
            MegatronRetroFinetuneModel,
            cfg,
            trainer,
            model_cfg,
            modify_confg_fn=_modify_config,
            save_restore_connector=save_restore_connector,
        )
    else:
        model = MegatronRetroFinetuneModel(cfg.model, trainer=trainer)
    trainer.fit(model)