Training YOLOv8 on custom dataset for detection

Question

I am trying to train the model here is my code:

from ultralytics import YOLO

model = YOLO("yolov8n.yaml")

results = model.train(data=r"C:\Users\abdal\PycharmProjects\bus_car_detection\venv\config.yaml", epochs=20)

config.yaml

path: E:\dataset
train: train\images
val: valid\images

nc: 2
names:
  0: Bus
  1: Car

Each time I run the code I got the following error:

RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

I tried to run that code local using PyCharm IDE.