Add Hyperparameter evolution Tuner() class (#4599)

docs/integrations/ray-tune.md

@@ -30,27 +30,31 @@ To install the required packages, run:
 
 !!! tip "Installation"
 
-    ```bash
-    # Install and update Ultralytics and Ray Tune packages
-    pip install -U ultralytics "ray[tune]"
+    === "CLI"
 
-    # Optionally install W&B for logging
-    pip install wandb
-    ```
+        ```bash
+        # Install and update Ultralytics and Ray Tune packages
+        pip install -U ultralytics "ray[tune]"
+    
+        # Optionally install W&B for logging
+        pip install wandb
+        ```
 
 ## Usage
 
 !!! example "Usage"
 
-    ```python
-    from ultralytics import YOLO
+    === "Python"
 
-    # Load a YOLOv8n model
-    model = YOLO("yolov8n.pt")
+        ```python
+        from ultralytics import YOLO
     
-    # Start tuning hyperparameters for YOLOv8n training on the COCO128 dataset
-    result_grid = model.tune(data="coco128.yaml")
-    ```
+        # Load a YOLOv8n model
+        model = YOLO('yolov8n.pt')
+        
+        # Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset
+        result_grid = model.tune(data='coco8.yaml', use_ray=True)
+        ```
 
 ## `tune()` Method Parameters
 
@@ -62,7 +66,7 @@ The `tune()` method in YOLOv8 provides an easy-to-use interface for hyperparamet
 | `space`         | `dict, optional` | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters.                     |               |
 | `grace_period`  | `int, optional`  | The grace period in epochs for the [ASHA scheduler](https://docs.ray.io/en/latest/tune/api/schedulers.html) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10            |
 | `gpu_per_trial` | `int, optional`  | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs.                                                                                                           | None          |
-| `max_samples`   | `int, optional`  | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely.                                                                                                 | 10            |
+| `iterations`    | `int, optional`  | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely.                                                                                                 | 10            |
 | `**train_args`  | `dict, optional` | Additional arguments to pass to the `train()` method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations.                                                                                           | {}            |
 
 By customizing these parameters, you can fine-tune the hyperparameter optimization process to suit your specific needs and available computational resources.
@@ -110,7 +114,8 @@ In this example, we demonstrate how to use a custom search space for hyperparame
     # Run Ray Tune on the model
     result_grid = model.tune(data="coco128.yaml",
                              space={"lr0": tune.uniform(1e-5, 1e-1)},
-                             epochs=50)
+                             epochs=50,
+                             use_ray=True)
     ```
 
 In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco128.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.