Fast AutoAugment is a NeurIPS 2019 research project that uses density-matching-based search to efficiently discover optimal data augmentation policies for training convolutional neural networks. The implementation learns augmentation strategies via a smarter search mechanism that is orders of magnitude faster than the original AutoAugment while achieving comparable performance across CIFAR and ImageNet benchmarks. It provides pre-trained model weights and supports distributed training.