Models used in pipelines cause complete PC shutdown
Hello, as already described in the title, I have some problems with complete failures of my PC on which I develop.Briefly about me, I am a student who wants to build a small analysis framework that uses models from the Hugging Face Transformers library. The specs of my PC are:Processor i5-8400 2.8 GHz16GB RAMWindows 10Dockert Desktop is installed on it.In my development environment I have installed pytoch and the transformer library. My program works smoothly here. To make the whole thing deplyoable I use a base image from docker hub.“huggingface/transformers-pytorch-cpu:4.18.0”I also built my own docker image which gave me the same error with large amounts of data.I simply use 2 models in pipelines that I initialize beforehand and then repeatedly feed with data.Here is the code of my pipelines and where I use it.from transformers import pipeline


class Model1:
    def __init__(self):
        self.generator = pipeline(model="model1name/")

    def analyze(self, body):
        return self.generator(body)# .decode('utf-8'))


class Model2:
    def __init__(self):
        self.generator = pipeline(model="model2name/")

    def analyze(self, body):
        return self.generator(body)# .decode('utf-8'))m1 = Model1()
m2 = Model2()

 if "text" in current_text:
      results.append(m1.analyze(current_text["text"])[0])
      results.append(m2.analyze(current_text["text"])[0])I also have a MongoDB and use Rabbitmq for data transfer. Unfortunately I can’t explain my problem completely because I can’t find any error logs. If I give my system a large amount of data, all my cores run at 100% and after a short time the entire PC simply shuts down. Windows itself doesn’t give me an error report.Maybe I did something wrong when initializing, or am I leaving too much computing capacity in the pipelines?