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[docs] make empty_cache device-agnostic (#34774)

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make device-agnostic
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Fanli Lin 2024-11-19 01:58:26 +08:00 committed by GitHub
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commit 8568bf1bcf
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1 changed files with 4 additions and 2 deletions
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docs/source/en/training.md
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@ -287,9 +287,10 @@ model.fit(tf_dataset)
At this point, you may need to restart your notebook or execute the following code to free some memory:
```py
from accelerate.utils.memory import clear_device_cache
del model
del trainer
torch.cuda.empty_cache()
clear_device_cache()
```
Next, manually postprocess `tokenized_dataset` to prepare it for training.
@ -364,8 +365,9 @@ Lastly, specify `device` to use a GPU if you have access to one. Otherwise, trai
```py
>>> import torch
>>> from accelerate.test_utils.testing import get_backend
>>> device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
>>> device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
>>> model.to(device)
```