# Cannot train train transformer on Mac/MPS **URL:** \ **Category:** Beginners\ **Created:** [June 1, 2024, 10:50am UTC](https://discuss.huggingface.co/t/cannot-train-train-transformer-on-mac-mps/89738 "2024-06-01T10:50:59Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![innomadic](https://avatars.discourse-cdn.com/v4/letter/i/b4bc9f/32.png) [@innomadic](https://discuss.huggingface.co/u/innomadic)\ **Post date:** [June 1, 2024, 10:50am UTC](https://discuss.huggingface.co/t/cannot-train-train-transformer-on-mac-mps/89738/1 "2024-06-01T10:50:59Z")
I’m trying to replicate this notebook on my MacBook: > **[Google Colab](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb#)** I get stuck here: ```auto %%time print(model.device) trainer.train() ``` With these results: ```auto Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future version. Using `--per_device_train_batch_size` is preferred. mps:0 0%| | 0/15228 [00:13:2 File ~/Documents/sombert/.venv/lib/python3.12/site-packages/transformers/trainer.py:1912, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1910 hf_hub_utils.enable_progress_bars() 1911 else: -> 1912 return inner_training_loop( 1913 args=args, 1914 resume_from_checkpoint=resume_from_checkpoint, 1915 trial=trial, 1916 ignore_keys_for_eval=ignore_keys_for_eval, 1917 ) File ~/Documents/sombert/.venv/lib/python3.12/site-packages/transformers/trainer.py:2245, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 2242 self.control = self.callback_handler.on_step_begin(args, self.state, self.control) 2244 with self.accelerator.accumulate(model): -> 2245 tr_loss_step = self.training_step(model, inputs) 2247 if ( 2248 args.logging_nan_inf_filter 2249 and not is_torch_xla_available() 2250 and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step)) 2251 ): 2252 # if loss is nan or inf simply add the average of previous logged losses 2253 tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged) ... 2262 # remove once script supports set_grad_enabled 2263 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type) -> 2264 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) RuntimeError: Placeholder storage has not been allocated on MPS device! ``` As I’ve looks around, it seems like at one point it was required to send the model to the MPS device explicitly but now it should happen automatically. I printed out the model’s device in the sample above. Here are my versions: ```auto v3.12.3 (.venv) ❯ pip list Package Version ------------------ ----------- accelerate 0.30.1 aiohttp 3.9.5 aiosignal 1.3.1 appnope 0.1.4 asttokens 2.4.1 attrs 23.2.0 certifi 2024.2.2 charset-normalizer 3.3.2 comm 0.2.2 datasets 2.19.1 debugpy 1.8.1 decorator 5.1.1 dill 0.3.8 executing 2.0.1 filelock 3.14.0 frozenlist 1.4.1 fsspec 2024.3.1 huggingface-hub 0.23.2 idna 3.7 ipykernel 6.29.4 ipython 8.24.0 jedi 0.19.1 Jinja2 3.1.4 jupyter_client 8.6.2 jupyter_core 5.7.2 MarkupSafe 2.1.5 matplotlib-inline 0.1.7 mpmath 1.3.0 multidict 6.0.5 multiprocess 0.70.16 nest-asyncio 1.6.0 networkx 3.3 numpy 1.26.4 packaging 24.0 pandas 2.2.2 parso 0.8.4 pexpect 4.9.0 pip 24.0 platformdirs 4.2.2 prompt_toolkit 3.0.45 psutil 5.9.8 ptyprocess 0.7.0 pure-eval 0.2.2 pyarrow 16.1.0 pyarrow-hotfix 0.6 Pygments 2.18.0 python-dateutil 2.9.0.post0 pytz 2024.1 PyYAML 6.0.1 pyzmq 26.0.3 regex 2024.5.15 requests 2.32.3 safetensors 0.4.3 six 1.16.0 stack-data 0.6.3 sympy 1.12.1 tokenizers 0.19.1 torch 2.3.0 tornado 6.4 tqdm 4.66.4 traitlets 5.14.3 transformers 4.42.0.dev0 typing_extensions 4.12.0 tzdata 2024.1 urllib3 2.2.1 wcwidth 0.2.13 xxhash 3.4.1 yarl 1.9.4 ``` Any ideas?