On Efficient Constructions of Checkpoints

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-Checkpoint uses 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 28{\texttimes} and recovery speedup up to 5. 77{\texttimes} over a state-of-the-art algorithm (SCAR).

Authors

• Yu Chen 0036
• Zhenming Liu
• Bin Ren 0002
• Xin Jin 0008

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