Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with computation to make your model fit into memory more easily. For feed-forward models we were able to fit more than 10x larger models onto our GPU, at only a 20% increase in computation time. The memory intensive part of training deep neural networks is computing the gradient of the loss by backpropagation. By checkpointing nodes in the computation graph defined by your model, and recomputing the parts of the graph in between those nodes during backpropagation, it is possible to calculate this gradient at reduced memory cost. When training deep feed-forward neural networks consisting of n layers, we can reduce the memory consumption to O(sqrt(n)) in this way, at the cost of performing one additional forward pass (see e.g. Training Deep Nets with Sublinear Memory Cost, by Chen et al. (2016)). This repository provides an implementation of this functionality in Tensorflow, using the Tensorflow graph editor to automatically rewrite the computation graph of the backward pass.