How to set all tensors to cuda device?

If you have GPU on our machine, then you can implement our model faster than CPU. But you have to set the tensors to the Cuda device so that the machine can utilize the GPU for training the model. Let's understand why we need to set tensors to the CUDA device and how to do it.

GPUs consist of thousands of small cores that perform optimization for parallel processing. Training deep learning models including matrix multiplication, and gradient computations becomes time-consuming on the CPU. That's why we need to move the tensors to the CUDA device so that the execution can be parallel and efficient. Modern GPUs give large amounts of high-speed memory. It is another advantage that helps in the fastest training.

There are several ways to set all the tensors to the CUDA device. We can use torch.cuda.set_device() function. Or we can use the .to() function to set all the tensors to the CUDA device. 

first set the device name.

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

First example

import torchtorch.cuda.set_device(device) # All tensors created after this line will be placed on the GPU by defaulttensor = torch.randn(10, 10) print(tensor.device) 

Second example

import torchtensor = torch.randn(10, 10) # Move the tensor to the GPUtensor = tensor.to(device) print(tensor.device) 

An Example can be how to set all the tensors to the CUDA device and train a Pytorch model on the CUDA device. Here, we define the device name. Then set tensor type to the device. Then create some data and set those to the device. Then perform neural network on those. 

import torch # Get the current CUDA devicedevice = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Set the default tensor type to the CUDA devicetorch.set_default_tensor_type(device) # Create some datax = torch.randn(10, 10)y = torch.randn(10, 10) # Move the data to the CUDA devicex = x.to(device)y = y.to(device) # Train the modeloptimizer = torch.optim.Adam(model.parameters(), lr=0.01) for epoch in range(10):     optimizer.zero_grad()     # Make a prediction     y_pred = model(x)     # Calculate the loss     loss = (y_pred - y).pow(2).mean()     # Backpropagate the loss     loss.backward()     # Update the model parameters     optimizer.step()

In the above code, first, you move the tensor into your device(CPU/CUDA), then perform model training with the specified data. If you have a GPU, then ensure that you have configured the CUDA and CuDNN accurately. Hope, you have gained precise knowledge about this topic.