Fix HUB session with DDP training (#13103)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/engine/trainer.py

@@ -48,6 +48,7 @@ from ultralytics.utils.torch_utils import (
     one_cycle,
     select_device,
     strip_optimizer,
+    torch_distributed_zero_first,
 )
 
 
@@ -127,7 +128,8 @@ class BaseTrainer:
 
         # Model and Dataset
         self.model = check_model_file_from_stem(self.args.model)  # add suffix, i.e. yolov8n -> yolov8n.pt
-        self.trainset, self.testset = self.get_dataset()
+        with torch_distributed_zero_first(RANK):  # avoid auto-downloading dataset multiple times
+            self.trainset, self.testset = self.get_dataset()
         self.ema = None
 
         # Optimization utils init
@@ -143,6 +145,9 @@ class BaseTrainer:
         self.csv = self.save_dir / "results.csv"
         self.plot_idx = [0, 1, 2]
 
+        # HUB
+        self.hub_session = None
+
         # Callbacks
         self.callbacks = _callbacks or callbacks.get_default_callbacks()
         if RANK in {-1, 0}:

ultralytics/hub/session.py

@@ -72,7 +72,7 @@ class HUBTrainingSession:
         try:
             session = cls(identifier)
             assert session.client.authenticated, "HUB not authenticated"
-            if args:
+            if args and not identifier.startswith(f"{HUB_WEB_ROOT}/models/"):  # not a HUB model URL
                 session.create_model(args)
                 assert session.model.id, "HUB model not loaded correctly"
             return session

ultralytics/utils/callbacks/hub.py

@@ -9,7 +9,7 @@ from ultralytics.utils import LOGGER, RANK, SETTINGS
 
 def on_pretrain_routine_start(trainer):
     """Create a remote Ultralytics HUB session to log local model training."""
-    if RANK in {-1, 0} and SETTINGS["hub"] is True and not getattr(trainer, "hub_session", None):
+    if RANK in {-1, 0} and SETTINGS["hub"] is True and SETTINGS["api_key"] and trainer.hub_session is None:
         trainer.hub_session = HUBTrainingSession.create_session(trainer.args.model, trainer.args)
 
 

ultralytics/utils/dist.py

@@ -37,6 +37,7 @@ if __name__ == "__main__":
     cfg = DEFAULT_CFG_DICT.copy()
     cfg.update(save_dir='')   # handle the extra key 'save_dir'
     trainer = {name}(cfg=cfg, overrides=overrides)
+    trainer.args.model = "{getattr(trainer.hub_session, 'model_url', trainer.args.model)}"
     results = trainer.train()
 """
     (USER_CONFIG_DIR / "DDP").mkdir(exist_ok=True)

ultralytics/utils/torch_utils.py

@@ -43,8 +43,8 @@ TORCHVISION_0_13 = check_version(TORCHVISION_VERSION, "0.13.0")
 
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
-    """Decorator to make all processes in distributed training wait for each local_master to do something."""
-    initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
+    """Ensures all processes in distributed training wait for the local master (rank 0) to complete a task first."""
+    initialized = dist.is_available() and dist.is_initialized()
     if initialized and local_rank not in {-1, 0}:
         dist.barrier(device_ids=[local_rank])
     yield