Pytorch NLP model doesn’t use GPU when making inference

Question

I have a NLP model trained on Pytorch to be run in Jetson Xavier. I installed Jetson stats to monitor usage of CPU and GPU. When I run the Python script, only CPU cores work on-load, GPU bar does not increase. I have searched on Google about that with keywords of " How to check if pytorch is using the GPU?" and checked results on stackoverflow.com etc. According to their advices to someone else facing similar issue, cuda is available and there is cuda device in my Jetson Xavier. However, I don’t understand why GPU bar does not change, CPU core bars go to the ends.

I don’t want to use CPU, it takes so long to compute. In my opinion, it uses CPU, not GPU. How can I be sure and if it uses CPU, how can I change it to GPU?

Note: Model is taken from huggingface transformers library. I have tried to use cuda() method on the model. (model.cuda()) In this scenario, GPU is used but I can not get an output from model and raises exception.

Here is the code:

from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
import torch

BERT_DIR = "savasy/bert-base-turkish-squad"    

tokenizer = AutoTokenizer.from_pretrained(BERT_DIR)
model = AutoModelForQuestionAnswering.from_pretrained(BERT_DIR)
nlp=pipeline("question-answering", model=model, tokenizer=tokenizer)


def infer(question,corpus):
    try:
        ans = nlp(question=question, context=corpus)
        return ans["answer"], ans["score"]
    except:
        ans = None
        pass

    return None, 0

Answer 1

The problem has been solved with loading pipeline containing device parameter:

nlp = pipeline("question-answering", model=BERT_DIR, device=0)