GPU usage in docs
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Assessment
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Newbie friendliness
- 78/100
- Issue type
- Documentation
- Clarity
- Clearly specified
- Activity status
- Quiet
- Tech stack
- python, pytorch
- Domain
- documentation
Research direction
Open docs/source/getting_started/quickstart.md at the referenced line and compare the documented accelerator value with the accepted PyTorch device types shown in the traceback. Check torchTextClassifiers.py around line 401 and run the quickstart or its documentation checks; done means the quickstart uses a working accelerator value and no longer produces the reported device-string error.
Written by the indexing model from the issue text.
Description
accelerator="gpu" does not work for me, but accelerator="cuda" works instead (as in torch.device).
Details of the error:
Traceback (most recent call last):
File "path/to/train_script.py", line xxx, in main
ttc.train(
~~~~~~~~~^
X_train,
^^^^^^^^
...<4 lines>...
verbose=True,
^^^^^^^^^^^^^
)
^
File path/to/torchTextClassifiers/torchTextClassifiers.py", line 401, in train
device = torch.device(training_config.accelerator)
RuntimeError: Expected one of cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, maia, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone device type at start of device string: gpu
- Dominant language
- Python
- Stars
- 23
- Forks
- 7
- PR merge metrics
- No merged PRs in 30d
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