Add tuner.py to Docs (#3689)

docs/reference/yolo/utils/tuner.md

@@ -0,0 +1,9 @@
+---
+description: Optimize YOLO models' hyperparameters with Ultralytics YOLO's `run_ray_tune` function using Ray Tune and ASHA scheduler.
+keywords: Ultralytics YOLO, Hyperparameter Tuning, Ray Tune, ASHAScheduler, Optimization, Object Detection
+---
+
+## run_ray_tune
+---
+### ::: ultralytics.yolo.utils.tuner.run_ray_tune
+<br><br>

mkdocs.yml

@@ -351,6 +351,7 @@ nav:
               - plotting: reference/yolo/utils/plotting.md
               - tal: reference/yolo/utils/tal.md
               - torch_utils: reference/yolo/utils/torch_utils.md
+              - tuner: reference/yolo/utils/tuner.md
           - v8:
               - classify:
                   - predict: reference/yolo/v8/classify/predict.md

setup.py

@@ -46,7 +46,7 @@ setup(
             'mkdocs-material',
             'mkdocstrings[python]',
             'mkdocs-redirects',  # for 301 redirects
-            'mkdocs-ultralytics-plugin',  # for meta descriptions and images, dates and authors
+            'mkdocs-ultralytics-plugin>=0.0.21',  # for meta descriptions and images, dates and authors
         ],
         'export': ['coremltools>=6.0', 'openvino-dev>=2022.3', 'tensorflowjs'],  # automatically installs tensorflow
     },

ultralytics/yolo/engine/model.py

@@ -389,15 +389,7 @@ class YOLO:
 
     def tune(self, *args, **kwargs):
         """
-        Runs hyperparameter tuning using Ray Tune.
+        Runs hyperparameter tuning using Ray Tune. See ultralytics.yolo.utils.tuner.run_ray_tune for Args.
-
-        Args:
-            data (str): The dataset to run the tuner on.
-            space (dict, optional): The hyperparameter search space. Defaults to None.
-            grace_period (int, optional): The grace period in epochs of the ASHA scheduler. Defaults to 10.
-            gpu_per_trial (int, optional): The number of GPUs to allocate per trial. Defaults to None.
-            max_samples (int, optional): The maximum number of trials to run. Defaults to 10.
-            train_args (dict, optional): Additional arguments to pass to the `train()` method. Defaults to {}.
 
         Returns:
             (dict): A dictionary containing the results of the hyperparameter search.