No CUDA GPUs are available
i get this error from the method during the model training process. i am using the google colab to run the code. the google colab dont have any GPU. Is their any other way i can make the code run without requiring cuda cpu.
How can i fix this error ?
def train_model(model, train_loader, val_loader, epoch, loss_function, optimizer, path, early_stop):
   
    # GPU
   #device = torch.device(&quot;cuda:0&quot; if torch.cuda.is_available() else &quot;cpu&quot;)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#     device = torch.device(&quot;cpu&quot;)
    device = torch.device(&quot;cuda&quot;)
    model = model.to(device)
    

    patience, eval_loss = 0, 0
    
    # train
    for i in range(epoch):
        total_loss, count = 0, 0
        y_pred = list()
        y_true = list()
        for idx, (x, y) in tqdm(enumerate(train_loader), total=len(train_loader)):
            x, y = x.to(device), y.to(device) 
            u, m = model(x)
            predict = torch.sigmoid(torch.sum(u*m, 1))
            y_pred.extend(predict.cpu().detach().numpy())
            y_true.extend(y.cpu().detach().numpy())
            loss = loss_function(predict, y.float())
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            total_loss += float(loss)
            count += 1
            
        train_auc = roc_auc_score(np.array(y_true), np.array(y_pred))
        torch.save(model, path.format(i+1))
        print(&quot;Epoch %d train loss is %.3f and train auc is %.3f&quot; % (i+1, total_loss / count, train_auc))
    
        # verify
        total_eval_loss = 0
        model.eval()
        count_eval = 0
        val_y_pred = list()
        val_true = list()
        for idx, (x, y) in tqdm(enumerate(val_loader), total=len(val_loader)):
            x, y = x.to(device), y.to(device)
            u, m = model(x)
            predict = torch.sigmoid(torch.sum(u*m, 1))
            val_y_pred.extend(predict.cpu().detach().numpy())
            val_true.extend(y.cpu().detach().numpy())
            loss = loss_function(predict, y.float())
            total_eval_loss += float(loss)
            count_eval += 1
        val_auc = roc_auc_score(np.array(y_true), np.array(y_pred))
        print(&quot;Epoch %d val loss is %.3fand train auc is %.3f&quot; % (i+1, total_eval_loss / count_eval, val_auc))
        



Just remove the line where you create your torch.device() and remove all the .to(device) functions where you use it. Then you also don't need to write .cpu().detach() also. You can simply write predict.numpy() as such. When you write device = torch.device(&quot;cuda&quot;) you are creating a GPU device and you are then transferring your model and data into the GPU device when its not available. This is the reason for the error.
