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")
    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)
output = model(input_tensor)
loss = loss_function(output, target)

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.

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