How can I optimize my PyTorch model for GPU performance?
To optimize your PyTorch model for GPU, you can start by ensuring that your tensors and models are moved to the GPU using .to(device)
.
What are some common techniques for improving performance?
Common techniques include using mixed precision training, optimizing data loading with DataLoader
, and minimizing GPU memory usage.
How does mixed precision training work?
Mixed precision training uses both 16-bit and 32-bit floating-point types to speed up training and reduce memory usage.
Can I use multiple GPUs for training?
Yes, you can use multiple GPUs by leveraging torch.nn.DataParallel
or torch.distributed
for more advanced setups.
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