diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 989934a6..6fcad522 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -241,7 +241,7 @@ jobs:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
   Conda:
-    if: github.repository == 'ultralytics/ultralytics' && (github.event_name != 'workflow_dispatch' || github.event.inputs.conda == 'true' || github.event_name == 'schedule')
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true')
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
diff --git a/tests/test_cuda.py b/tests/test_cuda.py
index 7f3d87df..f11f8fa0 100644
--- a/tests/test_cuda.py
+++ b/tests/test_cuda.py
@@ -79,7 +79,6 @@ def test_predict_sam():
 
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
-@pytest.mark.skipif(True, reason="RayTune Error pyarrow.lib.ArrowInvalid: URI has empty scheme: './runs/tune'")
 def test_model_ray_tune():
     with contextlib.suppress(RuntimeError):  # RuntimeError may be caused by out-of-memory
         YOLO('yolov8n-cls.yaml').tune(use_ray=True,
diff --git a/tests/test_python.py b/tests/test_python.py
index a4299bf8..a2afdcb1 100644
--- a/tests/test_python.py
+++ b/tests/test_python.py
@@ -41,7 +41,7 @@ def test_model_methods():
     model.to('cpu')
     model.fuse()
     model.clear_callback('on_train_start')
-    model._reset_callbacks()
+    model.reset_callbacks()
 
     # Model properties
     _ = model.names
diff --git a/ultralytics/engine/model.py b/ultralytics/engine/model.py
index 4ebbf7ba..5973e73f 100644
--- a/ultralytics/engine/model.py
+++ b/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__
diff --git a/ultralytics/utils/tuner.py b/ultralytics/utils/tuner.py
index 6126a987..2ca6e5a3 100644
--- a/ultralytics/utils/tuner.py
+++ b/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()