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Parameter "device" in SupervisedRunner has no effect

See original GitHub issue

🐛 Bug Report

When running with device=torch.device('cuda:0') or device='cuda:0' model still uses all the GPUs. When running with device=torch.device('cpu') or device='cpu' an error RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cpu appears.

How To Reproduce

Run any sort of training using SupervisedRunner(device=torch.device('cpu')) or SupervisedRunner(device=torch.device('cuda:0')) on a machine with multiple GPUs.

Environment

PyTorch version: 1.5.0
OS: Ubuntu 18.04
Python version: 3.7
CUDA runtime version: 10.2```

Issue Analytics

State:
Created 3 years ago
Reactions:2
Comments:7 (6 by maintainers)

Top GitHub Comments

1reaction

yhn112commented, May 8, 2020

I certainly do use CUDA_VISIBLE_DEVICES. It doesn’t help me deal with such severe bugs in such a popular framework. =)

0reactions

Scitatorcommented, May 19, 2020

now, it’s your turn 😂 https://github.com/catalyst-team/catalyst/pull/809

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