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

Second example

import torch
tensor = torch.randn(10, 10) 
# Move the tensor to the GPU
tensor = 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 device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
# Set the default tensor type to the CUDA device
torch.set_default_tensor_type(device) 
# Create some data
x = torch.randn(10, 10)
y = torch.randn(10, 10) 
# Move the data to the CUDA device
x = x.to(device)
y = y.to(device) 
# Train the model
optimizer = 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.