torch.Tensor.cpu — PyTorch 1.10.1 documentation
pytorch.org › generated › torchtorch.Tensor.cpu. Tensor.cpu(memory_format=torch.preserve_format) → Tensor. Returns a copy of this object in CPU memory. If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned. Parameters. memory_format ( torch.memory_format, optional) – the desired memory format of ...
torch.Tensor — PyTorch 1.10.1 documentation
pytorch.org › docs › stabletorch.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 ...
How to switch Pytorch between cpu and gpu
ofstack.com › python › 40337Sep 12, 2021 · Therefore, to get back to the point, when we use x.cuda to allocate gpu, we only need to use torch.cuda.is_available plus a judgment. When we want to use cpu, we can control the command line parameters of the execution program: if torch.cuda.is_available(): x= x.cuda()
torch.Tensor — PyTorch 1.10.1 documentation
https://pytorch.org/docs/stable/tensorstorch.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 ...