What are the differences between using CPU and GPU in PyTorch?

What are the differences between using CPU and GPU in PyTorch?

Using a CPU is generally slower for deep learning tasks compared to a GPU, which is optimized for parallel processing.

Can you explain why GPUs are better for training neural networks?

GPUs can handle thousands of operations simultaneously, making them ideal for the matrix calculations in neural networks.

Are there any scenarios where using a CPU is preferable?

Yes, for smaller models or when working with limited data, CPUs can be more efficient and cost-effective.

How can I check if my PyTorch code is running on a GPU?

You can use torch.cuda.is_available() to check if a GPU is available and tensor.device to see where your tensors are allocated.