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torch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...

Each torch.Tensor has a torch.dtype , torch.device , and torch.layout .

obj (Tensor or Storage) – object allocated on the selected device. ... Gets the cuda capability of a device. Parameters ... Initialize PyTorch's CUDA state.

Tensor. get_device () -> Device ordinal (Integer). For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.

We will get an error message. ... Use “get_device()” to check ... https://discuss.pytorch.org/t/how-to-know-on-which-gpu-the-tensor-is/31793 ...

The torch.device enables you to specify the device type responsible to load a tensor into memory. The function expects a string argument ...

Quoting the reply from a PyTorch developer: That's not possible. ... then whenever you get a new Tensor or Module # this won't copy if they are already on ...

21.09.2020 · I have a torch model that i'm trying to port from CPU do be device independent. setting the device parameter when creating tensors, or calling model.to(device) to move a full model to the target device solves part of the problem, but there are some "left behind" tensors (like variables created during the forward call)

23.07.2020 · I am new to Pytorch, but it seems pretty nice. My only question was when to use tensor.to(device) or Module.nn.to(device).. I was reading the documentation on this topic, and it indicates that this method will move the tensor or model to the specified device. But I was not clear for what operations this is necessary, and what kind of errors I will get if I don't use .to() at the …

device_of. class torch.cuda.device_of(obj) [source] Context-manager that changes the current device to that of given object. You can use both tensors and storages as arguments. If a given object is not allocated on a GPU, this is a no-op. Parameters. obj ( Tensor or Storage) – object allocated on the selected device. device_of.

08.01.2018 · In Pytorch you can allocate tensors to devices when you create them. By default, tensors get allocated to the cpu. To check where your tensor is allocated do: # assuming that 'a' is a tensor created somewhere else a.device # returns the device where the tensor is allocated Note that you cannot operate on tensors allocated in different devices.

14.07.2017 · Hi, I have such a simple method in my model def get_normal(self, std): if <here I need to know which device is used> : eps = torch.cuda.FloatTensor(std.size()).normal_() else: eps = torch.FloatTensor(std.size()).normal_() return Variable(eps).mul(std) To work efficiently, it needs to know which device is currently used (CPU or GPU). I was looking for something like …

then whenever you get a new Tensor or Module # this won't copy if they are already on the desired device input = data.to(device) model ...

29.08.2019 · To get a value from non single element tensor we have to be careful: The next example will show that PyTorch tensor residing on CPU shares the same storage as numpy array na. Example: Shared storage. import torch a = torch.ones ( (1,2)) print (a) na = a.numpy () na [0] [0]=10 print (na) print (a) Output:

torch.Tensor.get_device¶ Tensor. get_device ( ) -> Device ordinal (Integer ) ¶ For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.

PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable Nvidia GPU. PyTorch supports various sub-types of Tensors. Note that the term "tensor" here does not carry the same meaning as in mathematics or physics. The meaning of the word in those areas is only tangentially related to the one in Machine Learning.

For legacy reasons, a device can be constructed via a single device ordinal, which is treated as a cuda device. This matches Tensor.get_device(), which returns an ordinal for cuda tensors and is not supported for cpu tensors. >>>

PyTorch provides a core data structure, the tensor, a multi-dimensional ... You can find detailed hardware specification in our hardware documentation.