This repository documents various techniques for improving PyTorch model training and inference. It covers topics like model design patterns, convolution-BN fusion for faster inference, model compression and quantization methods, memory-efficient training strategies such as mixed precision with apex, and accelerated data loading using tar format with IterableDataset. Content is organized by category with links to reference papers and resources.