What are some lesser-known features of PyTorch that can greatly enhance model performance?
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One lesser-known feature of PyTorch is the ability to use custom GPU kernels. By writing custom CUDA kernels in PyTorch, you can significantly optimize performance for certain operations.
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Additionally, PyTorch provides support for DataParallel, allowing you to train models on multiple GPUs without much hassle. This can greatly speed up model training and inference.
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Another lesser-known feature is the PyTorch Autograd Profiler, which helps identify performance bottlenecks in your models. It allows you to visualize the time taken by each operation and optimize your code accordingly.
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