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22.07.2020 · Data on CPU and model on GPU, or vice-versa, will result in a Runtime error. You can set a variable device to cuda if it's available, else it will be set to cpu, and then transfer data and model to device : import torch device = 'cuda' if torch.cuda.is_available () else 'cpu' model.to (device) data = data.to (device) Share

Apr 21, 2020 · There are basicially 2 ways to move a tensor and a module (notice that a model is a model too) to a specific device in PyTorch. The first (old) way is to call the methods Tensor.cpu and/or Tensor.cuda. The second (new) way is to call the method Tensor.to .

There are basicially 2 ways to move a tensor and a module (notice that a model is a model too) to a specific device in PyTorch.

torch.Tensor.to — PyTorch 1.10.0 documentation torch.Tensor.to Tensor.to(*args, **kwargs) → Tensor Performs Tensor dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of self.to (*args, **kwargs). Note If the self Tensor already has the correct torch.dtype and torch.device, then self is returned.

LightningModules know what device they are on! construct tensors on the device directly to avoid CPU->Device transfer. t = tensor.rand(2, 2).cuda()# bad (self is lightningModule)t = tensor.rand(2,2, device=self.device)# good I had a similar issue to create tensors this helped me. I hope it will help you too.

To transfer a "CPU" tensor to "GPU" tensor, simply do: cpuTensor = cpuTensor.cuda(). This would take this tensor to default GPU device.

Feb 28, 2020 · You can move a Tensor to a specific device by doing x_cuda1 = x.to("cuda:1"). This is a differentiable operation. So if x requires gradient, then any op on x_cuda1 will contribute to the gradients toward x. For the .grad field, you can do x_cuda1.grad = x.grad.to("cuda:1").

Moving tensors around CPU / GPUs. Every Tensor in PyTorch has a to() member function. It's job is to put the tensor on which it's called to a certain device ...

move_to_device¶ · x (dictionary of list of tensors) – object (e.g. dictionary) of tensors to move to device · device (Union[str, torch.DeviceObjType]) – device, ...

LongTensor(1).random_(0, 10) a.to(device="cuda"). Is this per design, maybe I am simple missing something to convert tensor from CPU to CUDA ...

A torch tensor defined on CPU can be moved to GPU and vice versa. ... tensor device (cpu/cuda) print("Tensor device:", x.device) # Move ...

Mar 08, 2019 · If you are pushing tensors to a device or host, you have to reassign them: a = a.to(device='cuda') nn.Modules push all parameters, buffers and submodules recursively and don’t need the assignment.

torch.Tensor.to — PyTorch 1.10.0 documentation torch.Tensor.to Tensor.to(*args, **kwargs) → Tensor Performs Tensor dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of self.to (*args, **kwargs). Note If the self Tensor already has the correct torch.dtype and torch.device, then self is returned.

08.03.2019 · Moving tensor to cuda. Intel_Novel (Intel Novel) March 8, 2019, 9:07pm ... If you are pushing tensors to a device or host, you have to reassign them: a = a.to(device='cuda') nn.Modules push all parameters, buffers and submodules recursively and don’t need the …

21.04.2020 · There are basicially 2 ways to move a tensor and a module (notice that a model is a model too) to a specific device in PyTorch. The first (old) way is to call the methods Tensor.cpu and/or Tensor.cuda. The second (new) way is to call the method Tensor.to .