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Use CUDA_VISIBLE_DEVICES (not “DEVICE”). You have to set it before you launch the program – you can't do it from within the program. 4 Likes.

I use docker in a server, the GPU is randomly assigned to me, and cuda_visible_device is forbidden in pbs or shell document because it will conflict with server's assignment. How can I …

Nov 16, 2017 · os.environ [“CUDA_VISIBLE_DEVICES”] =“1,2” , only GPU 1 is used. At least, such a line in Python has its own effect. It can control the use of GPUs. However, It is supposed to make GPU 1 and 2 available for the task, but the result is that only GPU 1 is available. Even when GPU 1 is out of memory, GPU 2 is not used.

I had imported a file where the cuda device was getting initialized. I set the os.environ["CUDA_VISIBLE_DEVICES"] on the very top and it ...

21.09.2021 · Use CUDA_VISIBLE_DEVICES=0,1 python your_script.py to set all available GPU devices for all processes. I’m not aware of the intrinsecs of torch.cuda.set_device. Just to mention when you pass device_ids this is a list which enlist the available gpus from the pytorch pov.. For example, if you call CUDA_VISIBLE_DEVICES=5,7,9 there will be 3 gpus from 0 to 2.

device¶ class torch.cuda. device (device) [source] ¶ Context-manager that changes the selected device. Parameters. device (torch.device or int) – device index to select. It’s a no-op if this argument is a negative integer or None.

torch.cuda.set_device. 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. device ( torch.device or int) – selected device. This function is a no-op if this argument is negative.

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. This function is a no-op if this argument is negative. Next Previous

Sets the random number generator state of all devices. ... caching allocator so that those can be used in other GPU application and visible in nvidia-smi .

It shouldn't happen. That is a CUDA flag. Once set, PyTorch will never have access to the excluded device(s).

device¶ class torch.cuda. device (device) [source] ¶ Context-manager that changes the selected device. Parameters. device (torch.device or int) – device index to select. It’s a no-op if this argument is a negative integer or None.

14.03.2017 · two things you did wrong: there shouldn’t be semicolon. with the semicolon, they are on two different lines, and python won’t see it. even with the correct command CUDA_VISIBLE_DEVICES=3 python test.py, you won’t see torch.cuda.current_device() = 3, because it completely changes what devices pytorch can see.So in pytorch land device#0 is …

Sep 21, 2021 · Use CUDA_VISIBLE_DEVICES=0,1 python your_script.py to set all available GPU devices for all processes. I’m not aware of the intrinsecs of torch.cuda.set_device. Just to mention when you pass device_ids this is a list which enlist the available gpus from the pytorch pov. For example, if you call

import os, torch print(torch.cuda.is_available()) # True ... that in this case, pytorch will count all available devices as 0,1,2 ).

This way I only set the GPU devices to be used for all processes, not each process. But torch.cuda.set_device() can set GPU device for each ...

I'm trying to specify specify which single GPU to run code on within Python code, by setting the GPU index visible to PyTorch.

If you're not able to use CUDA_VISIBLE_DEVICES then the exact details depend on how you're performing inference. Generally you can assign a model or tensor to a specific cuda device using .to(f'cuda:{device_id}') (for example x = x.to('cuda:0') ).

Sets the random number generator state of all devices. Parameters. new_states (Iterable of torch.ByteTensor) – The desired state for each device. torch.cuda.

torch.cuda is used to set up and run CUDA operations. It keeps track of the currently selected GPU, and all CUDA tensors you allocate will by default be ...

18.08.2020 · A work around would be setting CUDA_VISIBLE_DEVICES in main.py before loading any cuda-related packages. Note that the recommended way to use DDP is one-process-per-device, i.e., each process should exclusively run on one GPU. If you want this, you need to set CUDA_VISIBLE_DEVICES to a different value for each subprocess.

24.10.2018 · use_gpu = torch.cuda.is_available () and not os.environ ['USE_CPU'] Then you can start your program as python runme.py to run on GPU if available, and USE_CPU=1 python3 runme.py to force CPU execution (or make it semi-permanent by export USE_CPU=1 ). I tried changing the Cuda visible devices with. You can also try running your code with CUDA ...

16.05.2019 · 🐛 Bug PyTorch is not using the GPU specified by CUDA_VISIBLE_DEVICES To Reproduce Run the following script using command CUDA_VISIBLE_DEVICES=3 python test.py ... 1.1.0.dev20190516 Is debug build: No CUDA used to build PyTorch: 10.0.130 OS: Ubuntu 18.04.1 LTS GCC version: (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0 CMake ...

May 16, 2019 · 🐛 Bug PyTorch is not using the GPU specified by CUDA_VISIBLE_DEVICES To Reproduce 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) prin...

May 14, 2019 · os.environ [“CUDA_VISIBLE_DEVICES”]=“0,2,5” to use only special devices (note, that in this case, pytorch will count all available devices as 0,1,2 ) Setting these environment variables inside a script might be a bit dangerous and I would also recommend to set them before importing anything CUDA related (e.g. PyTorch).

14.05.2019 · os.environ [“CUDA_VISIBLE_DEVICES”]=“0,2,5” to use only special devices (note, that in this case, pytorch will count all available devices as 0,1,2 ) Setting these environment variables inside a script might be a bit dangerous and I would also recommend to set them before importing anything CUDA related (e.g. PyTorch).

16.11.2017 · But in my code, when i use. os.environ [“CUDA_VISIBLE_DEVICES”] =“1,2”. , only GPU 1 is used. At least, such a line in Python has its own effect. It can control the use of GPUs. However, It is supposed to make GPU 1 and 2 available for the task, but the result is that only GPU 1 is available. Even when GPU 1 is out of memory, GPU 2 is ...