PyTorch is a great instrument for use in research and production areas, which is clearly shown by the adoption of this deep learning framework by Stanford University, Udacity, SalelsForce, Tesla, etc.. However, every tool requires investing time into mastering skills to use it with the maximum efficiency. After using PyTorch for more than two years, I decided to summarize my experience with this deep learning library.
Efficient — (of a system or machine) achieving maximum productivity with minimum wasted effort or expense. (Oxford Languages)
This part of the Efficient PyTorch series gives general tips for identifying and eliminating I/O and CPU bottlenecks. The second part will reveal some tips on efficient tensor operations. The third part — on efficient model debugging techniques.