Comments (4)
Support for different GPU devices should be totally fine. Are you sure that torch.cuda.device_count()
is greater than one in your case? Can you also post all the information about PyTorch from the issue template? There may be an issue with your PyTorch version that causes this.
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torch.cuda.device_count()
prints out 2
.
- OS: Ubuntu 16.04
- PyTorch version: 0.4.1
- How you installed PyTorch (conda, pip, source): conda
- Python version: 3.6.5
- CUDA/cuDNN version: 8.0/6.0
- GPU models and configuration: 1080Ti
- GCC version (if compiling from source): 5.4
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Update: Sorry, you were right! I didn't know I needed to set the device via setCudaDevice
for kernels to work properly. It all works fine now :)
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For people having trouble with multi gpu support, see https://discuss.pytorch.org/t/c-cuda-extension-with-multiple-gpus/91241
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Related Issues (20)
- www.facebook.com/rozi.khatun.961
- This repo can not compile using Pytorch 1.6.0 HOT 1
- This repo can not compile using Pytorch 1.6.0 HOT 1
- A question about backward propagation speed in cpp extension in tutorial
- Error when building cpp extension HOT 1
- AttributeError: module 'depthwise' has no attribute 'forward'
- cuda does not install HOT 17
- How does the layer of C++ extensions translate to TorchScript or onnx? HOT 1
- Cannot Unpack, Too Many Items
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- Writing a custom C++ extension with variants for both CPU and GPU? HOT 2
- Custom CUDA operator only work well on cuda:0 HOT 3
- How can I access data in cuda kernel like in pytorch?
- error LNK2001 undefined reference to `__cudaRegisterLinkedBinary
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- [feature request] Instruction on how to setup compile-env for Windows
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