This repository provides a memory optimization technique for training deep neural networks in TensorFlow. It works by strategically placing checkpoints in the computation graph and recomputing intermediate activations during backpropagation instead of storing them all in memory. The implementation uses the TensorFlow graph editor to automatically rewrite the backward pass computation graph. For feed-forward networks, this allows fitting models more than 10x larger onto a GPU with approximately 20% additional computation time.