为什么AI在PC上训练自定义模型的时间比我的笔记本还要长？

Question

我正在为分类做一个定制的训练模型--我在一台有2 CPU和32 RAM的电脑上测试了训练的模型，但是它比没有GPU的笔记本电脑8G Ram要慢，如屏幕截图所示，我的PC需要将近1小时，我的笔记本需要10分钟，为什么会这样呢？

这是我带windows 10 https://ibb.co/1zQn9Vm的笔记本电脑的屏幕

这是带有windows 11 https://ibb.co/j8WTTL0的PC的屏幕。

这是我的代码：

target = []
images =[]
flat_data =[]



DATADIR = r"Images"
CATEGORIES = ['cat1', 'cat2', 'cat3', 'cat4']

for cat in CATEGORIES:
class_num = CATEGORIES.index(cat)
path = os.path.join(DATADIR, cat)
for img in os.listdir(path):
  img2 = Image.open(os.path.join(path, img))
  compression_image = img2.info['compression']
  if(compression_image == 'group4'):
      img2.save(os.path.join(path, img), compression='tiff_lzw')                                                      
  img_array = imread(os.path.join(path, img))
  img_resized = resize(img_array, (200 , 200))
  flat_data.append(img_resized.flatten())
  images.append(img_resized)
  target.append(class_num)
  
  
 flat_data = np.array(flat_data)
 target = np.array(target)
 images = np.array(images)

 from sklearn.model_selection import  train_test_split
 x_train, x_test, y_train, y_test= train_test_split(flat_data, 
 target, test_size=0.3, random_state= 109)

 from sklearn.model_selection import  GridSearchCV
 from sklearn import svm

 param_grid = [
{'C': [1, 10, 100, 1000], 'kernel': ['linear']},
{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': 
['rbf']}
]

svc = svm.SVC(probability= True)
clf = GridSearchCV(svc, param_grid)
clf.fit(x_train, y_train)

Answer 1

在我添加了这个参数之后，它工作得很好。

GridSearchCV try setting n_jobs=-1