src/transformers/sagemaker/training_args_sm.py/SageMakerTrainingArguments/_setup_devices
class SageMakerTrainingArguments:    def _setup_devices(self) -> "torch.device":
        logger.info("PyTorch: setting up devices")
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
            logger.warning(
                "torch.distributed process group is initialized, but local_rank == -1. "
                "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
            )
        if self.no_cuda:
            device = torch.device("cpu")
            self._n_gpu = 0
        elif is_sagemaker_model_parallel_available():
            local_rank = smp.local_rank()
            device = torch.device("cuda", local_rank)
            self._n_gpu = 1
        elif is_sagemaker_dp_enabled():
            import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

            torch.distributed.init_process_group(backend="smddp", timeout=self.ddp_timeout_delta)
            self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))
            device = torch.device("cuda", self.local_rank)
            self._n_gpu = 1
        elif self.local_rank == -1:
            # if n_gpu is > 1 we'll use nn.DataParallel.
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
            # trigger an error that a device index is missing. Index 0 takes into account the
            # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
            # will use the first GPU in that env, i.e. GPU#1
            device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
            # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
            # the default value.
            self._n_gpu = torch.cuda.device_count()
        else:
            # Here, we'll use torch.distributed.
            # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
            if not torch.distributed.is_initialized():
                torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta)
            device = torch.device("cuda", self.local_rank)
            self._n_gpu = 1

        if device.type == "cuda":
            torch.cuda.set_device(device)

        return device