ultralytics 8.0.141 create new SettingsManager (#3790)

docs/modes/train.md

@@ -21,20 +21,20 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Argum
     Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n.yaml')  # build a new model from YAML
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
         model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
-        
+
         # Train the model
         model.train(data='coco128.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"
-    
+
         ```bash
         # Build a new model from YAML and start training from scratch
         yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
@@ -53,18 +53,18 @@ The training device can be specified using the `device` argument. If no argument
 !!! example "Multi-GPU Training Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
-        
+
         # Train the model with 2 GPUs
         model.train(data='coco128.yaml', epochs=100, imgsz=640, device=[0, 1])
         ```
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model using GPUs 0 and 1
         yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 device=0,1
@@ -79,18 +79,18 @@ To enable training on Apple M1 and M2 chips, you should specify 'mps' as your de
 !!! example "MPS Training Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
-        
+
         # Train the model with 2 GPUs
         model.train(data='coco128.yaml', epochs=100, imgsz=640, device='mps')
         ```
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model using GPUs 0 and 1
         yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 device=mps
@@ -111,18 +111,18 @@ Below is an example of how to resume an interrupted training using Python and vi
 !!! example "Resume Training Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('path/to/last.pt')  # load a partially trained model
-        
+
         # Resume training
         model.train(resume=True)
         ```
     === "CLI"
-    
+
         ```bash
         # Resume an interrupted training
         yolo train resume model=path/to/last.pt
@@ -239,4 +239,4 @@ tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
 
 This will load TensorBoard and direct it to the directory where your training logs are saved.
 
-After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.
+After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.