class_docstr: A DDP Strategy which uses ray as backend.
class_docstr_tok: ["A","DDP","Strategy","which","uses","ray","as","backend","."]
code: def _setup_env_vars(self):
      # Get rank 0 worker address and port for DDP connection.
      os.environ["MASTER_ADDR"] = ray.get(
          self.workers[0].get_node_ip.remote()
      )
      os.environ["MASTER_PORT"] = str(
          ray.get(self.workers[0].execute.remote(find_free_port))
      )
  
      # Set environment variables for remote workers.
      keys = [
          "PL_GLOBAL_SEED",
          "PL_TORCH_DISTRIBUTED_BACKEND",
          "MASTER_ADDR",
          "MASTER_PORT",
      ]
      values = [os.getenv(k) for k in keys]
      ray.get(
          [w.set_env_vars.remote(keys, values) for w in self.workers]
      )
  
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 4 for calling the RayStrategy obj's underlying member method code with example usage: obj._setup_env_vars() without return types