"In short, all memory that is allocated for a given *model identifier*, *batch size* and *sequence length* is measured in a separate process. This way it can be ensured that there is no previously unreleased memory falsely included in the measurement. One should also note that the measured memory even includes the memory allocated by the CUDA driver to load PyTorch and TensorFlow and is, therefore, higher than library-specific memory measurement function, *e.g.* this one for [PyTorch](https://pytorch.org/docs/stable/cuda.html#torch.cuda.max_memory_allocated).\n",
"In short, all memory that is allocated for a given *model identifier*, *batch size* and *sequence length* is measured in a separate process. This way it can be ensured that there is no previously unreleased memory falsely included in the measurement. One should also note that the measured memory even includes the memory allocated by the CUDA driver to load PyTorch and TensorFlow and is, therefore, higher than library-specific memory measurement function, *e.g.* this one for [PyTorch](https://pytorch.org/docs/stable/cuda.html#torch.cuda.max_memory_allocated).\n",
"\n",
"\n",
"Alright, let's analyze the results. It can be noted that the models `aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2` and `deepset/roberta-base-squad2` require significantly less memory than the other three models. Besides `mrm8488/longformer-base-4096-finetuned-squadv2` all models more or less follow the same memory consumption pattern with `aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2` seemingly being able to better scale to larger sequence lengths. \n",
"Alright, let's analyze the results. It can be noted that the models `aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2` and `deepset/roberta-base-squad2` require significantly less memory than the other three models. Besides `mrm8488/longformer-base-4096-finetuned-squadv2` all models more or less follow the same memory consumption pattern with `aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2` seemingly being able to better scale to larger sequence lengths. \n",
"`mrm8488/longformer-base-4096-finetuned-squadv2` is a *Longformer* model, which makes use of *LocalAttention* (check this blog post to learn more about local attention) so that the model scales much better to longer input sequences.\n",
"`mrm8488/longformer-base-4096-finetuned-squadv2` is a *Longformer* model, which makes use of *LocalAttention* (check [this](https://huggingface.co/blog/reformer) blog post to learn more about local attention) so that the model scales much better to longer input sequences.\n",
"\n",
"\n",
"For the sake of this notebook, we assume that the longest required input will be less than 512 tokens so that we settle on the models `aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2` and `deepset/roberta-base-squad2`. \n",
"For the sake of this notebook, we assume that the longest required input will be less than 512 tokens so that we settle on the models `aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2` and `deepset/roberta-base-squad2`. \n",
"Alright, that's it! Now you should be able to benchmark your favorite models on your favorite configurations. \n",
"Alright, that's it! Now you should be able to benchmark your favorite models on your favorite configurations. \n",
"\n",
"\n",
"Transparency for the computational cost of a model is becoming more and more important. Feel free to share your results with the community on a shared spreadsheet or by tweeting us @huggingface 🤗."
"Feel free to share your results with the community [here](https://github.com/huggingface/transformers/blob/master/examples/benchmarking/README.md) or by tweeting us https://twitter.com/HuggingFace 🤗."
