How can I optimize my PyTorch model for better performance?
You can optimize your PyTorch model by using techniques like mixed precision training, model pruning, and quantization.
What is mixed precision training and how does it help?
Mixed precision training uses both 16-bit and 32-bit floating point types to speed up training and reduce memory usage.
Can you explain model pruning?
Model pruning involves removing weights from your model that contribute little to its performance, making it smaller and faster.
What tools can I use to profile my model's performance?
You can use tools like PyTorch's built-in profiler, TensorBoard, or NVIDIA's Nsight Systems for performance profiling.
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