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221 def cuda(xs, gpu_id): 222 if torch.cuda.is_available(): 223 if not isinstance(xs, (list, tuple)): 224 return xs.cuda(int(gpu_id[0])) 225 else: 226 return [x.cuda(int(gpu_id[0])) for x in xs] 227 return xs
124 def to_cuda(list_modules): 125 for m in list_modules: 126 m.cuda()
50 def _tensor_to_cuda(x): 51 if x.is_cuda: 52 return x 53 else: 54 return x.cuda()
30 @pytest.mark.skipif(not torch.cuda.is_available(), reason="Require cuda") 31 def test_to_device_cuda(): 32 obj = {"a": [torch.tensor([0, 1])]} 33 obj2 = to_device(obj, "cuda") 34 assert obj2["a"][0].device == torch.device("cuda:0")
770 def cuda(self, device_id=None): 771 """ 772 This method operates identically to :func:`torch.nn.Module.cuda`. 773 774 Args: 775 device_id (:obj:`str`, optional): 776 Device ID of GPU to use. 777 Returns: 778 :obj:`~gpytorch.lazy.LazyTensor`: 779 a new LazyTensor identical to ``self``, but on the GPU. 780 """ 781 new_args = [] 782 new_kwargs = {} 783 for arg in self._args: 784 if hasattr(arg, "cuda"): 785 new_args.append(arg.cuda(device_id)) 786 else: 787 new_args.append(arg) 788 for name, val in self._kwargs.items(): 789 if hasattr(val, "cuda"): 790 new_kwargs[name] = val.cuda(device_id) 791 else: 792 new_kwargs[name] = val 793 return self.__class__(*new_args, **new_kwargs)