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18.11.2019 · I have to stack some my own layers on different kinds of pytorch models with different devices. E.g. A is a cuda model and B is a cpu model (but I don't know it before I get the device type). Then the new models are C and D respectively, where.

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 …

Gets the cuda capability of a device. ... Initialize PyTorch's CUDA state. ... with a multi-GPU model, this function is insufficient to get determinism.

device property to the models. As mentioned by Kani (in the comments), if the all the parameters in the model are on the same device, one could use next ...

how to check weather my model is on gpu in pytorch ... pytorch get cuda device name · pytorch check if layer is on gpu · python check cuda is available ...

if there's a new attribute similar to model.device as is the case for ... so is next(network.parameters()).device simply getting the device ...

The next two arguments are important. The requires_grad argument tells PyTorch that we will want to compute gradients with respect to logits, because we want to learn its values. The device argument says where to store the array. There are a couple of functions below that will want to know what the parameters of our model are.

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I have to stack some my own layers on different kinds of pytorch models with different devices. E.g. A is a cuda model and B is a cpu model (but ...

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.

We will get an error message. It is a problem we can solve, of course. For example, I can put the model and new data to the same GPU device ...

We can change the default CUDA device easily by specifying the ID. torch.cuda.set_device(1) It is easy to make a few GPU devices invisible by setting the environment variables. import os os.environ[“CUDA_VISIBLE_DEVICES”] = “1,2,3” PyTorch model in GPU. There are three steps involved in training the PyTorch model in GPU using CUDA methods.

device to get the device. In that situation, you can also use next(model.parameters()).is_cuda to check if the model is on CUDA.

I think that's a bit too convoluted for such a simple thing. If you know all parameters are on a single device you can always do next(model.