16.03.2020 · This code is deprecated. Just do: def forward (self, inputs, hidden): embed_out = self.embeddings (inputs) logits = torch.zeros ( (self.seq_len, self.batch_size, self.vocab_size), device=inputs.device) Note that to (device) is cost-free if the tensor is already on the requested device. And do not use get_device () but rather device attribute.
Returns the currently selected Stream for the current device, given by current_device() , if device is None (default). torch.cuda. default_stream (device=None) ...
20.02.2020 · I’m having an issue of slow .to(device) transfer of a single batch. If I understood correctly, dataloader should be sampled from in the main training loop and only then (when the whole batch is gathered) should be transferred to gpu with .to(device) method of the batch tensor? My batch size is 32 samples x 64 features x 1000 length x 4 bytes (float32) / (1024*1024) = …
Device agnostic means that your code can run on any device. Code written by PyTorch to method can run on any different devices (CUDA / CPU). It is very difficult to write device-agnostic code in PyTorch of previous versions. Pytorch 0.4.0 makes code compatible. Pytorch 0.4.0 makes code compatibility very easy in two ways.
The expected device types in torch.device() are cpu, cuda, mkldnn, opengl, opencl, ideep, hip, msnpu. The device type should exist in the list of expected ...
Python queries related to “torch get device”. torch.device · pytorch cuda · torch get device · pytorch use gpu · torch.device() · pytorch gpu available · use ...
torch. to (device = None, dtype = None, non_blocking = False, copy = False, memory_format = torch.preserve_format) → Tensor Returns a Tensor with the specified device and (optional) dtype . If dtype is None it is inferred to be self.dtype .
device¶ class torch.cuda. device (device) [source] ¶ Context-manager that changes the selected device. Parameters. device (torch.device or int) – device index to select. It’s a no-op if this argument is a negative integer or None.
torch.to(other, non_blocking=False, copy=False) → Tensor. Returns a Tensor with same torch.dtype and torch.device as the Tensor other. When non_blocking, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. When copy is set, a new Tensor is created even when ...
19.12.2019 · Then run python setup.py install. import torch import my_extension x = torch.rand (3, 4) y = x.cuda () print (my_extension.run (y)) print (y) z = x.to (1) print (my_extension.run (z)) print (z) I do some simple check. The function inline bool CUDA_tensor_apply22 in my_extension_kernel.cu returns true. Could you try to get the current device ...
device¶ class torch.cuda. device (device) [source] ¶ Context-manager that changes the selected device. Parameters. device (torch.device or int) – device index to select. It’s a no-op if this argument is a negative integer or None.
Mar 17, 2020 · This code is deprecated. Just do: def forward (self, inputs, hidden): embed_out = self.embeddings (inputs) logits = torch.zeros ( (self.seq_len, self.batch_size, self.vocab_size), device=inputs.device) Note that to (device) is cost-free if the tensor is already on the requested device.
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.
Device agnostic means that your code can run on any device. Code written by PyTorch to method can run on any different devices (CUDA / CPU). It is very ...