def METHOD_NAME(cfg) -> None:
    logging.info("\n\n************** Experiment configuration ***********")
    logging.info(f'\n{OmegaConf.to_yaml(cfg)}')
    megatron_amp_o2 = cfg.model.get('megatron_amp_O2', False)
    with_distributed_adam = cfg.model.optim.get('name') == 'distributed_fused_adam'
    plugins = []
    strategy = NLPDDPStrategy(
        no_ddp_communication_hook=True,  # we don't use DDP for async grad allreduce
        gradient_as_bucket_view=cfg.model.gradient_as_bucket_view,
        find_unused_parameters=False,
    )
    if cfg.trainer.precision in [16, '16', 'bf16', '16-mixed', '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 and not with_distributed_adam:
            plugins.append(MegatronHalfPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler))
        else:
            plugins.append(PipelineMixedPrecisionPlugin(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)
    # load existing or init new soft prompt GPT model
    if cfg.model.get("restore_path", None):
        model = MegatronT5AdapterLearningModel.restore_from(
            cfg.model.restore_path, cfg.model, trainer=trainer, save_restore_connector=NLPSaveRestoreConnector()
        )
    else:
        model = MegatronT5AdapterLearningModel(cfg.model, trainer=trainer)
    trainer.fit(model)