>>> x = torch.randn(3, 4, 5, device='cuda:0') >>> x.get_device() 0 >>> x.cpu().get_device() # RuntimeError: get_device is not implemented for type torch.FloatTensor相关用法 Python PyTorch Tensor.unflatten用法及代码示例 Python PyTorch Tensor.register_hook用法及代码示例 Python ...
THCDeviceAllocator* allocator = state->cudaDeviceAllocator; *largestBlock =0;/* get info from CUDA first */cudaError_t ret = cudaMemGetInfo(freeBytes, totalBytes);if(ret!= cudaSuccess)returnret;intdevice; ret =cudaGetDevice(&device);if(ret!= cudaSuccess)returnret;/* not always true - ou...
这个包增加了对CUDA张量类型的支持，它实现了与CPU张量相同的功能，但是它们利用gpu进行计算。它是惰性...
I see some related discussions atpytorch/pytorch#40671. However, I don't think we have a problem with driver/cuda mismatch since it works when 1 card is disabled (if I understood correctly). Yeah, I don't think that's a problem. You are correct. It works on three cards or less, ...
# Get cpu or gpu device for training.device="cuda"iftorch.cuda.is_available()else"cpu"print(f"Using {device} device")# Define modelclassNeuralNetwork(nn.Module):def__init__(self):super(NeuralNetwork,self).__init__()self.flatten=nn.Flatten()self.linear_relu_stack=nn.Sequential(nn.Linea...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [MTIA] Support torch.cuda.get_device_capability equivalent API on MTIA · pytorch/pytorch@d833f49
print(a) # tensor([[7, 8, 9], [7, 8, 9]], device='cuda:0') 1. 2. 3. 4. 5. 输出: tensor([[7, 8, 9], [7, 8, 9]], device='cuda:0') 1. 2. copy.deepcopy() copy.deepcopy()函数是一个深复制函数。 所谓深复制,就是从输入变量完全复制可以相同的变量,无论怎么改变新...
device = torch.device('cuda', rank) torch.manual_seed(random_seed *num_gpus+ rank) // 建立nn.Module神经网络Generator模型 G以及移动均线G_ema平滑训练过程 G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs). train().requires_grad_(False).to(device) # subclass of tor...
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='display results') #实时显示 parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') #保...
def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id): if pin_memory: torch.cuda.set_device(device_id) while True: try: r = in_queue.get() except Exception: if done_event.is_set(): return raise if r is None: break if isinstance(r[1], ExceptionWrappe...