class_docstr: A DDP Strategy which uses ray as backend.
class_docstr_tok: ["A","DDP","Strategy","which","uses","ray","as","backend","."]
docstr: Creates a mapping of global ranks to local ranks.
docstr_tok: ["Creates","a","mapping","of","global","ranks","to","local","ranks","."]
code: def get_local_ranks(self):
      """Creates a mapping of global ranks to local ranks."""
      # Get the local ranks for all the workers and store as a dict.
      # First get the IP address of each remote worker.
      node_ips = ray.get([w.get_node_ip.remote() for w in self.workers])
      rank_counter_dict = defaultdict(int)  # type: ignore
      global_to_local = [None] * self.num_workers
      for global_rank in range(self.num_workers):
          ip = node_ips[global_rank]
          global_to_local[global_rank] = rank_counter_dict[ip]
          rank_counter_dict[ip] += 1
      return global_to_local
  
imports: import os
  import warnings
  from typing import Callable, Dict, List, Union, Any, Optional
  from collections import defaultdict
  import ray
  from ray.util.ml_utils.util import find_free_port
  import torch
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.nn.parallel.distributed import DistributedDataParallel
  import pytorch_lightning
  from pytorch_lightning.core.datamodule import LightningDataModule
  from pytorch_lightning.trainer.states import TrainerFn
  from pytorch_lightning.utilities import rank_zero_only
  from pytorch_lightning.utilities.apply_func import move_data_to_device
  from pytorch_lightning.strategies.launchers import _SpawnLauncher
  from pytorch_lightning.strategies import DDPSpawnStrategy
  from pytorch_lightning.core.optimizer import _configure_schedulers_automatic_opt
  from pytorch_lightning.core.optimizer import _configure_schedulers_manual_opt
  from pytorch_lightning.core.optimizer import _set_scheduler_opt_idx, _validate_scheduler_api
  from pytorch_lightning.core.optimizer import LightningOptimizer
  from .ray_envbase import RayEnvironment
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_12
  from bigdl.nano.deps.ipex.ipex_api import ipex_optimize
  from bigdl.nano.pytorch.dispatcher import _get_patch_status
label_desc: Use image node_id 5 for calling the RayStrategy obj's underlying member method code with example usage: obj.get_local_ranks() and returns: global_to_local