Using tensor.share_memory_() vs multiprocessing.Queue in PyTorch when training model across multiple processes
I'm using the multiprocessing package in pytorch to split the training across multiple processes. My x and y, train and test data are CUDA tensors. I'm trying to understand the difference between using the tensor.share_memory_() and the multiprocessing.Queue method to share cuda tensors. Which is preferred and why? 

Here's my current code using tensor.share_memory_(). What changes should I make?

def train(model, features, target, epochs=1000):

    X_train, x_test, Y_train, y_test = train_test_split(features,
                                                    target,
                                                    test_size=0.4,
                                                    random_state=0)

    Xtrain_ = torch.from_numpy(X_train.values).float().share_memory_()
    Xtest_ = torch.from_numpy(x_test.values).float().share_memory_()

    Ytrain_ = (torch.from_numpy(Y_train.values).view(1,-1)[0]).share_memory_()
    Ytest_ = (torch.from_numpy(y_test.values).view(1,-1)[0]).share_memory_()


    optimizer = optim.Adam(model.parameters(), lr = 0.01)
    loss_fn = nn.NLLLoss()


    for epoch in range(epochs):

        #training code here


target method ends here

mp.set_start_method('spawn')

model = Net()
model.share_memory()

processes = []

for rank in range(1):
    p = mp.Process(target=train, args=(model, features, target))
    p.start()
    processes.append(p)


Env details: Python-3 and Linux

They are the same.  torch.multiprocessing.Queue uses tensor.share_memory_() internally.
