If I run the following:
import torch
import sys
print('A', sys.version)
print('B', torch.__version__)
print('C', torch.cuda.is_available())
print('D', torch.backends.cudnn.enabled)
device = torch.device('cuda')
print('E', torch.cuda.get_device_properties(device))
print('F', torch.tensor([1.0, 2.0]).cuda())
I get this:
A 3.7.5 (default, Nov 7 2019, 10:50:52)
[GCC 8.3.0]
B 1.8.0.dev20210115+cu110
C True
D True
E _CudaDeviceProperties(name='GeForce RTX 3090', major=8, minor=6, total_memory=24267MB, multi_processor_count=82)
F
<stacktrace>
CUDA error: no kernel image is available for execution on the device
More info about my system:
GPU Requirements Release 20.01 supports CUDA compute capability 6.0 and higher.
Can PyTorch work with CUDA 11.6 or 11.7? Thanks. Yes, you could install the binaries with the CUDA 11.6 runtime by swapping the version from the provided install commands and you can of course build from source using both CUDA versions.
In PyTorch, the torch. cuda package has additional support for CUDA tensor types, that implement the same function as CPU tensors, but they utilize GPUs for computation. If you want a tensor to be on GPU you can call .
Found a fix for my problem here: https://github.com/pytorch/pytorch/issues/31285#issuecomment-739139454
pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html -U
Then my code snippet gives:
A 3.7.5 (default, Nov 7 2019, 10:50:52)
[GCC 8.3.0]
B 1.8.0.dev20210115+cu110
C True
D True
E _CudaDeviceProperties(name='GeForce RTX 3090', major=8, minor=6, total_memory=24267MB, multi_processor_count=82)
F tensor([1., 2.], device='cuda:0')
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