Unable to use --gpus to load model onto specific GPU
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Description
This is using vicuna-13b. I have not found a way to use `--gpus` to successfully utilize a specific GPU. With my workaround for issue #2359, I am able to use `num_gpus 1` which assigns GPU 0 by default.
Using `--gpus 1` or `--gpus 0` (with or without `--num-gpus 1`) fails with `RuntimeError: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "../aten/src/ATen/cuda/CUDAContext.cpp":50, please report a bug to PyTorch`
Interestingly, if I `export CUDA_VISIBLE_DEVICES=1` then the --gpus assignment works as expected, even when specifying `--gpus 0`, which allows me to run on the same machine two worker processes, one assigned to each GPU.
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Research direction
No files or tests are mentioned. Start by reproducing the vicuna-13b commands with --gpus 1 and --gpus 0, then compare them with the CUDA_VISIBLE_DEVICES=1 workaround. Done means selecting a specific GPU with --gpus works without the PyTorch device assertion failure.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100