larger dataset, larger GPU memory in the training phase? Is that correct?
NONE
does the trainer save it in GPU? sooo curious... how to fix it
2022-09-07T09:30:33Z
https://github.com/huggingface/datasets/issues/4944#issuecomment-1239144105
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NONE
It's my bad. didn't limit the input length
2022-09-07T12:34:45Z
https://github.com/huggingface/datasets/issues/4944#issuecomment-1239331078
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    from datasets import set_caching_enabled
    set_caching_enabled(False)
    for ds_name in ["squad","newsqa","nqopen","narrativeqa"]:
            train_ds = load_from_disk("../../../dall/downstream/processedproqa/{}-train.hf".format(ds_name))

        break
    train_ds = concatenate_datasets([train_ds,train_ds,train_ds,train_ds]) #operation 1


   trainer = QuestionAnsweringTrainer( #huggingface trainer
        model=model,
        args=training_args,
        train_dataset=train_ds,
        eval_dataset= None,
        eval_examples=None,
        answer_column_name=answer_column,
        dataset_name="squad",
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
    )

with operation 1, the GPU memory increases from 16G to 23G