如何确认YOLOv8训练正常且已启用GPU加速？

我是编程新手，因作业需求使用YOLOv8框架。CPU上可正常运行但训练速度极慢，尝试GPU加速的难度超出预期，原以为仅需下载库并小幅修改代码即可实现。目前已配置CUDA 12.1及包含cuDNN的PyTorch nightly版本，使用PyCharm开发环境。

当前训练时会反复输出模型结构信息，每个epoch启动前需等待约30秒，无法确认YOLOv8是否正常训练及是否已利用GPU加速。

使用的代码 #

from ultralytics import YOLO

model = YOLO("yolov8n.yaml")  

if __name__ == '__main__':
    results = model.train(data="config.yaml", epochs=100, device="0")

部分训练输出信息 #

from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.SPPF                  [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                            
 12                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                            
 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                   
 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                            
 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                            
 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 
 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.Detect                [80, [64, 128, 256]]          
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs

Ultralytics YOLOv8.0.23  Python-3.9.13 torch-2.3.0.dev20231231+cu121 CUDA:0 (NVIDIA GeForce RTX 2060, 6144MiB)
yolo\engine\trainer: task=detect, mode=train, model=None, data=config.yaml, epochs=100, patience=50, batch=16, imgsz=640, save=True, cache=False, device=0, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=runs\detect\train28
Overriding model.yaml nc=80 with nc=2

...（后续重复输出模型结构信息）

该输出会持续重复，因字符限制无法完整粘贴。

内容的提问来源于stack exchange，提问作者yagiz