A TensorFlow implementation of the idea from the paper Training Deep Nets with Sublinear Memory Cost
The code is messy and doesn't really work, but can be a starting point for someone who want to properly reimplement the idea in TensorFlow.
gradients function from TensorFlow gets as input a variable V and builds a computational graph that computes the gradient of V w.r.t. the parameters. The backward pass computational graph uses the values of all nodes of the forward computational graph. try.ipynb contains my reimplementation of gradients function. It also builds a graph to compute the gradient, but it only uses the values from the nodes listed in store_activations_set on the backward pass, all other necessary values are recomputed on the fly. Right now store_activations_set is hardcoded, but MemoryOptimizer.py is a still not working attempt to smartly choose what to put into store_activations_set based on the analysis of the forward computation graph.
try.ipynb builds a simple MNIST network and makes a few iterations of optimization. Use TensorBoard to explore the constructed graph.