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gpauloski authored Jul 6, 2021
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[![DOI](https://zenodo.org/badge/240976400.svg)](https://zenodo.org/badge/latestdoi/240976400)

Code for the papers "[Convolutional Neural Network Training with Distributed K-FAC](https://dl.acm.org/doi/10.5555/3433701.3433826)" and [KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks
](https://arxiv.org/abs/2107.01739)."
Code for the papers "[Convolutional Neural Network Training with Distributed K-FAC](https://dl.acm.org/doi/10.5555/3433701.3433826)" and "[KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks](https://arxiv.org/abs/2107.01739)."

This K-FAC implementation is designed to be scalable and efficiently distribute K-FAC computations to reduce training times in distributed environments.
The K-FAC distributed preconditioner supports [Horovod](https://github.com/horovod/horovod) and [torch.distributed](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for data-parallel training.
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