AssertionError: Torch not compiled with CUDA enabled

From https://pytorch.org/

to install pytorch on MacOS the following is stated :

conda install pytorch torchvision -c pytorch
# MacOS Binaries dont support CUDA, install from source if CUDA is needed

Why would want to install pytorch without cuda enabled ?

Reason I ask is I receive error :

--------------------------------------------------------------------------- AssertionError Traceback (most recent call last) in () 78 # predicted = outputs.data.max(1)[1] 79 ---> 80 output = model(torch.tensor([[1,1]]).float().cuda()) 81 predicted = output.data.max(1)[1] 82

~/anaconda3/lib/python3.6/site-packages/torch/cuda/init.py in _lazy_init() 159 raise RuntimeError( 160 "Cannot re-initialize CUDA in forked subprocess. " + msg) --> 161 _check_driver() 162 torch._C._cuda_init() 163 _cudart = _load_cudart()

~/anaconda3/lib/python3.6/site-packages/torch/cuda/init.py in _check_driver() 73 def _check_driver(): 74 if not hasattr(torch._C, '_cuda_isDriverSufficient'): ---> 75 raise AssertionError("Torch not compiled with CUDA enabled") 76 if not torch._C._cuda_isDriverSufficient(): 77 if torch._C._cuda_getDriverVersion() == 0:

AssertionError: Torch not compiled with CUDA enabled

when attempting to execute code :

x = torch.tensor([[0,0] , [0,1] , [1,0]]).float()
print(x)

y = torch.tensor([0,1,1]).long()
print(y)

my_train = data_utils.TensorDataset(x, y)
my_train_loader = data_utils.DataLoader(my_train, batch_size=2, shuffle=True)

# Device configuration
device = 'cpu'
print(device)

# Hyper-parameters 
input_size = 2
hidden_size = 100
num_classes = 2


learning_rate = 0.001

train_dataset = my_train

train_loader = my_train_loader

pred = []


for i in range(0 , model_iters) : 
    # Fully connected neural network with one hidden layer
    class NeuralNet(nn.Module):
        def __init__(self, input_size, hidden_size, num_classes):
            super(NeuralNet, self).__init__()
            self.fc1 = nn.Linear(input_size, hidden_size) 
            self.relu = nn.ReLU()
            self.fc2 = nn.Linear(hidden_size, num_classes)  

        def forward(self, x):
            out = self.fc1(x)
            out = self.relu(out)
            out = self.fc2(out)
            return out

    model = NeuralNet(input_size, hidden_size, num_classes).to(device)

    # Loss and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  

    # Train the model
    total_step = len(train_loader)
    for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(train_loader):  
            # Move tensors to the configured device
            images = images.reshape(-1, 2).to(device)
            labels = labels.to(device)

            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)

            # Backward and optimize
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
{:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

    output = model(torch.tensor([[1,1]]).float().cuda())

To fix this error I need to install pytorch from source with cuda already installed ?