code: def distributed_context(
      rank,
      opt,
      rank_offset=0,
      gpu=None,
      init_method="tcp://localhost:61337",
  ):
      """
      A context which wraps initialization of a distributed/multiprocessing run.
  
      Every process in the distributed run should launch with this. In true
      distributed setting you may wish to use slurm_distributed_context instead.
  
      :param int rank:
          This process's rank, less rank_offset.
      :param int rank_offset:
          Used as an offset of rank. Used between multiprocessing vs true distributed,
          and a hack around torch.multiprocessing.spawn being only used for the
          non-primary workers.
      :param opt:
          command line options
          distributed training setups on the same machine.
      :param int gpu:
          Which GPU to use. Defaults to using rank and local devices, but must be
          manually specified when using many-hosts.
      :param str init method:
          Init method, such as ``tcp://localhost:61337``. See torch.distributed docs.
      """
      # Set per-host options
      opt = copy.deepcopy(opt)
      # we need to manually adjust the rank differently in multiprocessing
      # and distributed train
      rank = rank + rank_offset
      opt["rank"] = rank
      if gpu is None:
          # default assumption is local GPUs
          gpu = rank % torch.cuda.device_count()
      opt["gpu"] = gpu
      # make sure we don't just use whatever GPU was saved in the model file
      if "override" not in opt:
          opt["override"] = {}
      opt["override"]["gpu"] = gpu
  
      # Suppress output of workers except the main host.
      if opt.get("verbose") or rank != 0:
          print_prefix = "rank:{:3d} |".format(rank)
      else:
          print_prefix = None
      suppress_output = not opt.get("verbose") and rank != 0
  
      with override_print(suppress_output, print_prefix):
          # perform distributed setup, ensuring all hosts are ready
          if opt["gpu"] != -1:
              torch.cuda.set_device(opt["gpu"])
          dist.init_process_group(
              backend="nccl",
              init_method=init_method,
              world_size=opt["distributed_world_size"],
              rank=rank,
          )
          logging.info("Distributed group initialized")
  
          # manual_seed can be a noop without this
          torch.cuda.init()
          # make sure all parameters will be in sync
          torch.manual_seed(42)
          # force a sync so that no one gets ahead, and all are seeded together
          sync_object(None)
  
          try:
              yield opt
          finally:
              dist.destroy_process_group()
  
file_path: ParlAI/parlai/utils/distributed.py
imports: import builtins
  import copy
  import os
  import pickle
  import contextlib
  import subprocess
  import socket
  import parlai.utils.logging
label_desc: Use image node_id 9 for calling a global function with example usage: distributed_context(rank, opt, rank_offset, gpu, init_method) without return types