code: def main(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)
  
file_path: NeMo/examples/nlp/language_modeling/megatron_retro_fine_tune.py
imports: import datetime
  import os
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks.timer import Timer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
  from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector
  from nemo.collections.nlp.models.language_modeling.megatron_retro_fine_tune_model import MegatronRetroFinetuneModel
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, NLPSaveRestoreConnector
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  from nemo.utils.exp_manager import StatelessTimer, exp_manager
label_desc: Use image node_id 3 for calling a global function with example usage: main(cfg) without return types