# Understanding gpu usage huggingface classification - Total optimization steps **URL:** \ **Category:** Beginners\ **Created:** [March 26, 2022, 2:50am UTC](https://discuss.huggingface.co/t/understanding-gpu-usage-huggingface-classification-total-optimization-steps/16102 "2022-03-26T02:50:45Z")\ **Posts on this page:** 1\ **Page:** 1

**Author:** ![nitempe](https://avatars.discourse-cdn.com/v4/letter/n/54ee81/32.png) [@nitempe](https://discuss.huggingface.co/u/nitempe)\ **Post date:** [March 26, 2022, 2:50am UTC](https://discuss.huggingface.co/t/understanding-gpu-usage-huggingface-classification-total-optimization-steps/16102/1 "2022-03-26T02:50:45Z")

I am training huggingface longformer for a classification problem and got below output. 1. I am confused about `Total optimization steps`. As I have 7000 training data points and 5 epochs and `Total train batch size (w. parallel, distributed & accumulation) = 64`, shouldn’t I get `7000*5/64` steps? that comes to `546.875`? why is it showing ` Total optimization steps = 545` 2. Why in the below output, there are 16 steps of `Input ids are automatically padded from 1500 to 1536 to be a multiple of config.attention_window: 512` then ` [23/545 14:24 < 5:58:16, 0.02 it/s, Epoch 0.20/5]`? what are these steps? ========================================================== ``` *****Running training***** Num examples = 7000 Num Epochs = 5 Instantaneous batch size per device = 4 Total train batch size (w. parallel, distributed & accumulation) = 64 Gradient Accumulation steps = 16 Total optimization steps = 545 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 Initializing global attention on CLS token... Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512 [23/545 14:24 < 5:58:16, 0.02 it/s, Epoch 0.20/5] Epoch Training Loss Validation Loss ```