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• W3088147970 abstract "Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkpoint constructions (called LC-Checkpoint). LC-Checkpoint simultaneously maximizes the compression rate and optimizes the recovery speed, under the assumption that SGD is used to train the model. LC-Checkpointuses quantization and priority promotion to store the most crucial information for SGD to recover, and then uses a Huffman coding to leverage the non-uniform distribution of the gradient scales. Our extensive experiments show that LC-Checkpoint achieves a compression rate up to $28times$ and recovery speedup up to $5.77times$ over a state-of-the-art algorithm (SCAR)." @default.
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• W3088147970 date "2020-09-28" @default.
• W3088147970 modified "2023-09-23" @default.
• W3088147970 title "On Efficient Constructions of Checkpoints." @default.
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• W3088147970 hasPublicationYear "2020" @default.
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