如何通过Nvidia独立显卡在Linux上运行脚本？

Question

我正在使用“人脸识别”库，并设置“cnn”参数

locations = face_recognition.face_locations(frame, model='cnn')

我怎样才能让这个脚本通过独立显卡而不是内置显卡工作？

跑过

optirun python main.py

和

primusrun python main.py

不返回任何结果，在检查使用情况时我只得到

nvidia-smi
...

|=======================================================================================|
|    0   N/A  N/A      7626      G   /usr/lib/Xorg                                 5MiB |
+---------------------------------------------------------------------------------------+

因此，脚本本身并不使用独立显卡

我对Linux很弱，但我想了解这个漂亮的操作系统，所以我将非常感谢您的回答。

剧本：

def detected():

    # data = pickle.loads(open('user_1_encodings.pickle', 'rb').read())

    cams = cv2.VideoCapture(0)
    not_identify = []

    while True:
        ret, frame = cams.read()

        locations = face_recognition.face_locations(frame, model='cnn')
        encodings = face_recognition.face_encodings(frame, locations)

        for face_encoding, face_location in zip(encodings, locations):
            # user_encodings = user_data['encodings']
            # result = face_recognition.compare_faces(user_encodings, face_encoding)
            result = False
            green = [0, 255, 0]
            blue = [255, 0, 0]
            red = [0, 0, 255]

            if any(result):
                print(result)
                match = user_data['name']
                left_top = (face_location[3], face_location[0])
                right_bottom = (face_location[1], face_location[2])
                cv2.rectangle(frame, left_top, right_bottom, green, 2)

                text_x = face_location[3]
                text_y = face_location[0] - 10  
                font_scale = 0.5
                font_thickness = 1
                font_face = cv2.FONT_HERSHEY_COMPLEX_SMALL
                text_size, _ = cv2.getTextSize(match, font_face, font_scale, font_thickness)
                text_width, text_height = text_size


                rect_left_top = (text_x-1, text_y+5)
                rect_right_bottom = (text_x + text_width+5, text_y - text_height-5)
                cv2.rectangle(frame, rect_left_top, rect_right_bottom, (255, 255, 255), -1)


                cv2.putText(frame, match, (text_x, text_y), font_face, font_scale, (255, 0, 0), font_thickness)

            else:
                print('NOT OK')
                print(face_location)
                match = 'Unknown'

                top, right, bottom, left = face_location
                face = frame[top:bottom, left:right]
                pil_image = Image.fromarray(face)

                print(pil_image)
                not_identify.append(pil_image)
                print(not_identify)
                if len(not_identify) > 3:
                    #
                    if result is False:
                        not_identify = []
                    else:
                        data = result
                        not_identify = []


                left_top = face_location[3], face_location[0]
                right_bottom = (face_location[1], face_location[2])

                cv2.rectangle(frame, left_top, right_bottom, red, 2)


            text_x = face_location[3]
            text_y = face_location[0] - 10  

            font_scale = 0.5
            font_thickness = 1
            font_face = cv2.FONT_HERSHEY_COMPLEX_SMALL
            text_size, _ = cv2.getTextSize(match, font_face, font_scale, font_thickness)
            text_width, text_height = text_size

            rect_left_top = (text_x-1, text_y+5)
            rect_right_bottom = (text_x + text_width+5, text_y - text_height-5)
            cv2.rectangle(frame, rect_left_top, rect_right_bottom, (255, 255, 255), -1)

            cv2.putText(frame, match, (text_x, text_y), font_face, font_scale, (255, 0, 0), font_thickness)

    cv2.imshow('scan', frame)
    k = cv2.waitKey(20)
    if k == ord('q'):
        break

if name == 'main': 检测到（）