setting CUDA_VISIBLE_DEVICES just has no effect #9158
github.com › pytorch › pytorchJul 03, 2018 · Ideally, train.py should look something like: import os os.environ ['CUDA_VISIBLE_DEVICES'] = "2" import json import torch import torch.nn as nn .... As @SsnL mentioned, the key is to add the two lines at the very top of the module. ibmua, PingjunChen, Xiaodong-Bran, gkoumasd, KyuminHwang, and vinven7 reacted with thumbs up emoji.
PyTorch is not using the GPU specified by CUDA_VISIBLE ...
github.com › pytorch › pytorchMay 16, 2019 · Run the following script using command CUDA_VISIBLE_DEVICES=3 python test.py # test.py import os import torch import time import sys print ( os . environ ) print ( torch . cuda . device_count ()) print ( torch . cuda . current_device ()) print ( os . getpid ()) sys . stdout . flush () device = torch . device ( 'cuda' ) a = torch . randn ( 10 , 10 , device = device ) os . system ( 'nvidia-smi' )
torch.cuda.device_count — PyTorch 1.11.0 documentation
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python - How to check if pytorch is using the GPU? - Stack ...
stackoverflow.com › questions › 48152674Jan 08, 2018 · or the GPU is being hidden by the environmental variable CUDA_VISIBLE_DEVICES. When the value of CUDA_VISIBLE_DEVICES is -1, then all your devices are being hidden. You can check that value in code with this line: os.environ['CUDA_VISIBLE_DEVICES'] If the above function returns True that does not necessarily mean that you are using the GPU. In Pytorch you can allocate tensors to devices when you create them.
torch.cuda.set_device — PyTorch 1.11.0 documentation
pytorch.org › generated › torchtorch.cuda.set_device — PyTorch 1.11.0 documentation torch.cuda.set_device torch.cuda.set_device(device) [source] Sets the current device. Usage of this function is discouraged in favor of device. In most cases it’s better to use CUDA_VISIBLE_DEVICES environmental variable. Parameters device ( torch.device or int) – selected device.