no code implementations • 21 Feb 2022 • Julia Gusak, Daria Cherniuk, Alena Shilova, Alexander Katrutsa, Daniel Bershatsky, Xunyi Zhao, Lionel Eyraud-Dubois, Oleg Shlyazhko, Denis Dimitrov, Ivan Oseledets, Olivier Beaumont
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training.
no code implementations • NeurIPS 2021 • Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova
Rematerialization and offloading are two well known strategies to save memory during the training phase of deep neural networks, allowing data scientists to consider larger models, batch sizes or higher resolution data.
no code implementations • 27 Nov 2019 • Julien Herrmann, Olivier Beaumont, Lionel Eyraud-Dubois, Julien Hermann, Alexis Joly, Alena Shilova
This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorithm.