cuda = torch.device('cuda') # 默认CUDA设备 cuda0 = torch.device('cuda:0') cuda2 = torch.device('cuda:2') # GPU 2 (these are 0-indexed) x =torch.tensor([1., 2.], device=cuda0) # x.device is device(type='cuda', index=0) y = torch.tensor([1., 2.]).cuda() # y.d...
The following actions use a deprecated Node.js version and will be forced to run on node20: actions/github-script@v6. For more info: https://github.blog/changelog/2024-03-07-github-actions-all-actions-will-run-on-node20-instead-of-node16-by-default/ Show more ...
device_type, device_index=0)[source] Context-manager to nest the device spec. Examples: with dali.device('cuda', 0): pass Parameters: device_type ({'cpu', 'gpu', 'cuda'}, required) – The type of device. device_index (int, optional, default=0) – The index of ...
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread: [2,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60:...
今天在训练扩散模型的时候发现噪声添加的第二个step就会出现Assertion 'srcIndex < srcSelectDimSize' failed导致CUDA error: device-side assert triggered. 原因是扩散模型在第一个step结尾扩散出的结果是以target dict为词典进行编码的,而将其作为第二个step的输入时读取的词典为input dict,由于target dict中的一些...
2. 分析用户提供的 'cuda' 为何不符合期望 当你看到错误信息 expected a torch.device with a specified index or an integer, but got:cuda 时,这通常意味着 PyTorch 期望一个更明确的设备指定方式,而不是仅仅使用 'cuda'。这可能是因为 PyTorch 无法确定你希望使用哪个 GPU,或者你的代码在某些上下文中(如多...
device=device(type="cuda", index=0), device=device, pin_memory=False, ) @@ -123,7 +142,7 @@ def forward( start=0, step=1, dtype=torch.int32, device=device(type="cuda"), device=device, requires_grad=False, ) @@ -135,7 +154,7 @@ def forward( start=0, step=1001, dtype=...
Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper__index_select) 数据不在同一设备 将device = d2l.try_gpu() 改为device = torch.device('cpu')...
nvidia-docker 上，所以本地可以看到 GPU 设备是 /dev/nvidia3/，正好落在第四块显卡上（因为是从0...
2.输入 list disk 查看磁盘,输入 select disk 0 3.输入 list partition ,输入 select partition n (Windows EFI分区,一般为260M) 4.输入 assign letter=J(分配盘符) 5.管理员模式打开记事本 6.打开 J盘/EFI 文件夹,删除Ubuntu文件夹 Install Nvidia and cuda ...