Hi, I am new to the machine learning community. For some reasons, I try to parallelly do inference using multi-core CPU and single GPU, however I just got following runtime errors.
THCudaCheck FAIL file=c:\a\w\1\s\tmp_conda_3.6_091443\conda\conda-bld\pytorch_1544087948354\work\torch\csrc\generic\StorageSharing.cpp line=232 error=71 : operation not supported
File "C:\Users\Anaconda3\lib\site-packages\torch\multiprocessing\reductions.py", line 213, in reduce_tensor
    (device, handle, storage_size_bytes, storage_offset_bytes) = storage._share_cuda_()
RuntimeError: cuda runtime error (71) : operation not supported at c:\a\w\1\s\tmp_conda_3.6_091443\conda\conda-bld\pytorch_1544087948354\work\torch\csrc\generic\StorageSharing.cpp:232

The following is a simplified example which can reproduce the errors.
import torch
from torch import nn

# model used to do inference
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(100,1)
        
    def forward(self,x):
        return self.fc1(x)
    
# class running inference
class A(object):
    def __init__(self):
        pass

    def do_something(self, model):
        # do something
        x = torch.randn(100).view(-1)
        print(model.forward(x))
    
    def run(self):
        mp = torch.multiprocessing.get_context('spawn')
        processes = []

        for i in range(2):
            p = mp.Process(target=self.do_something, args=(Model().cuda(),))
            processes.append(p)

        for p in processes:
            p.start()

if __name__ == '__main__':
    a = A()
    a.run()

It would be greatly appreciated if anyone can help solve this problem. By the way, my PC runs on Windows 10 with one GTX 1070 GPU.