diff --git a/ultralytics/engine/trainer.py b/ultralytics/engine/trainer.py
index cfdf80ed..2e7a7db4 100644
--- a/ultralytics/engine/trainer.py
+++ b/ultralytics/engine/trainer.py
@@ -227,7 +227,7 @@ class BaseTrainer:
         # LOGGER.info(f'DDP info: RANK {RANK}, WORLD_SIZE {world_size}, DEVICE {self.device}')
         os.environ["NCCL_BLOCKING_WAIT"] = "1"  # set to enforce timeout
         dist.init_process_group(
-            "nccl" if dist.is_nccl_available() else "gloo",
+            backend="nccl" if dist.is_nccl_available() else "gloo",
             timeout=timedelta(seconds=10800),  # 3 hours
             rank=RANK,
             world_size=world_size,
@@ -645,8 +645,8 @@ class BaseTrainer:
 
                 resume = True
                 self.args = get_cfg(ckpt_args)
-                self.args.model = str(last)  # reinstate model
-                for k in "imgsz", "batch":  # allow arg updates to reduce memory on resume if crashed due to CUDA OOM
+                self.args.model = self.args.resume = str(last)  # reinstate model
+                for k in "imgsz", "batch", "device":  # allow arg updates to reduce memory or update device on resume
                     if k in overrides:
                         setattr(self.args, k, overrides[k])
 
@@ -669,14 +669,11 @@ class BaseTrainer:
         if self.ema and ckpt.get("ema"):
             self.ema.ema.load_state_dict(ckpt["ema"].float().state_dict())  # EMA
             self.ema.updates = ckpt["updates"]
-        if self.resume:
-            assert start_epoch > 0, (
-                f"{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n"
-                f"Start a new training without resuming, i.e. 'yolo train model={self.args.model}'"
-            )
-            LOGGER.info(
-                f"Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs"
-            )
+        assert start_epoch > 0, (
+            f"{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n"
+            f"Start a new training without resuming, i.e. 'yolo train model={self.args.model}'"
+        )
+        LOGGER.info(f"Resuming training {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs")
         if self.epochs < start_epoch:
             LOGGER.info(
                 f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs."