Pytorch runtime error: Cuda Out of memory. Works fine with jupyter notebook but doesn't as a script
I have a special kind of problem. I am able to run the code in jupyter notebook perfectly fine with no OOM error. However when i run the same code as a script in linux it gives me the OOM error. Has anyone have the same issue. I tried gc.collect() and torch.cuda.empty_cache() inside the code and nothing helps.

It always gives me this error.
RuntimeError: CUDA out of memory. Tried to allocate 1.30 GiB (GPU 0; 7.79 GiB total capacity; 4.80 GiB already allocated; 922.69 MiB free; 6.12 GiB reserved in total by PyTorch

The code:

def lemmatize(phrase):
    """Return lematized words"""
    spa = spacy.load("en_core_web_sm")
    return " ".join([word.lemma_ for word in spa(phrase)])

def reading_csv(path_to_csv):
    """Return text column in csv"""
    data = pd.read_csv(path_to_csv)
    ctx_paragraph = []
    for txt in data['text']:
        if not pd.isna(txt):
            ctx_paragraph.append(txt)
    return ctx_paragraph

def processing_question(ques, paragraphs, domain_lemma_cache, domain_pickle):
    """Return answer"""
    #Lemmatizing whole csv text column
    lemma_cache = domain_lemma_cache
    if not os.path.isfile(lemma_cache):
        lemmas = [lemmatize(par) for par in tqdm(paragraphs)]
        df = pd.DataFrame(data={'context': paragraphs, 'lemmas': lemmas})
        df.to_feather(lemma_cache)
    df = pd.read_feather(lemma_cache)
    paragraphs = df.context
    lemmas = df.lemmas
    #Vectorizor cache
    if not os.path.isfile(VEC_PICKLE_LOC):
        vectorizer = TfidfVectorizer(
            stop_words='english', min_df=5, max_df=.5, ngram_range=(1, 3))
        vectorizer.fit_transform(lemmas)
        pickle.dump(vectorizer, open(VEC_PICKLE_LOC, "wb"))
    #Vectorized lemmas cache cache
    if not os.path.isfile(domain_pickle):
        tfidf = vectorizer.fit_transform(lemmas)
        pickle.dump(tfidf, open(domain_pickle, "wb"))
    vectorizer = pickle.load(open(VEC_PICKLE_LOC, "rb"))
    tfidf = pickle.load(open(domain_pickle, "rb"))
    question = ques
    query = vectorizer.transform([lemmatize(question)])
    (query &gt; 0).sum(), vectorizer.inverse_transform(query)
    scores = (tfidf * query.T).toarray()
    results = (np.flip(np.argsort(scores, axis=0)))
    qapipe = pipeline('question-answering',
                      model='distilbert-base-uncased-distilled-squad',
                      tokenizer='bert-base-uncased',
                      device=0)
    candidate_idxs = [(i, scores[i]) for i in results[0:10, 0]]
    contexts = [(paragraphs[i], s) for (i, s) in candidate_idxs if s &gt; 0.01]
    question_df = pd.DataFrame.from_records([{
        'question': question,
        'context':  ctx
    } for (ctx, s) in contexts])
    preds = qapipe(question_df.to_dict(orient="records"))
    answer_df = pd.DataFrame.from_records(preds)
    answer_df["context"] = question_df["context"]
    answer_df = answer_df.sort_values(by="score", ascending=False)
    return answer_df



I had a similar thing happen to me recently.
I would run my model in a Jupyter notebook, on a AWS EC2 p2.xlarge instance, and the model would run correctly.  Then, I would ssh into the same instance, and re-run a .py script of the same model, and receive the OOM errors that you described.
All I had to do was reset the kernal of the Jupyter notebook, to get the .py script to work.
