Fix Ray 2.7.0 breaking changes (#4964)

ultralytics/engine/model.py

@@ -392,17 +392,17 @@ class Model(nn.Module):
         """Clear all event callbacks."""
         self.callbacks[event] = []
 
+    def reset_callbacks(self):
+        """Reset all registered callbacks."""
+        for event in callbacks.default_callbacks.keys():
+            self.callbacks[event] = [callbacks.default_callbacks[event][0]]
+
     @staticmethod
     def _reset_ckpt_args(args):
         """Reset arguments when loading a PyTorch model."""
         include = {'imgsz', 'data', 'task', 'single_cls'}  # only remember these arguments when loading a PyTorch model
         return {k: v for k, v in args.items() if k in include}
 
-    def _reset_callbacks(self):
-        """Reset all registered callbacks."""
-        for event in callbacks.default_callbacks.keys():
-            self.callbacks[event] = [callbacks.default_callbacks[event][0]]
-
     # def __getattr__(self, attr):
     #    """Raises error if object has no requested attribute."""
     #    name = self.__class__.__name__

ultralytics/utils/tuner.py

@@ -2,8 +2,8 @@
 
 import subprocess
 
-from ultralytics.cfg import TASK2DATA, TASK2METRIC
-from ultralytics.utils import DEFAULT_CFG_DICT, LOGGER, NUM_THREADS
+from ultralytics.cfg import TASK2DATA, TASK2METRIC, get_save_dir
+from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, NUM_THREADS
 
 
 def run_ray_tune(model,
@@ -93,9 +93,10 @@ def run_ray_tune(model,
         Returns:
             None.
         """
-        model._reset_callbacks()
+        model.reset_callbacks()
         config.update(train_args)
-        model.train(**config)
+        results = model.train(**config)
+        return results.results_dict
 
     # Get search space
     if not space:
@@ -123,10 +124,12 @@ def run_ray_tune(model,
     tuner_callbacks = [WandbLoggerCallback(project='YOLOv8-tune')] if wandb else []
 
     # Create the Ray Tune hyperparameter search tuner
+    tune_dir = get_save_dir(DEFAULT_CFG, name='tune')
+    tune_dir.mkdir(parents=True, exist_ok=True)
     tuner = tune.Tuner(trainable_with_resources,
                        param_space=space,
                        tune_config=tune.TuneConfig(scheduler=asha_scheduler, num_samples=max_samples),
-                       run_config=RunConfig(callbacks=tuner_callbacks, storage_path='./runs/tune'))
+                       run_config=RunConfig(callbacks=tuner_callbacks, storage_path=tune_dir))
 
     # Run the hyperparameter search
     tuner.fit()