How to implement GPU acceleration in PyTorch?

To enable GPU acceleration in PyTorch, make sure you have installed a PyTorch version that supports GPU. Then you can run PyTorch code on the GPU by following these steps:

1. Check for available GPU devices.

import torch

if torch.cuda.is_available():
    device = torch.device("cuda")
    print("GPU is available")
else:
    device = torch.device("cpu")
    print("GPU is not available, using CPU instead")

1. Move the model and tensors to the GPU device.

model = YourModel().to(device)
input_tensor = torch.randn(1, 3, 224, 224).to(device)

1. Training and inference of models on GPU:

output = model(input_tensor)

1. parameters of the model
2. someone or something that seeks to improve or enhance a particular process or outcome
3. execute()

optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
optimizer.zero_grad()
output = model(input_tensor)
loss = loss_function(output, target)
loss.backward()
optimizer.step()

By following the steps above, you can implement GPU acceleration in PyTorch and leverage the parallel computing power of GPUs to speed up model training and inference.