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http://dx.doi.org/10.17661/jkiiect.2021.14.3.201

Design for Deep Learning Configuration Management System using Block Chain  

Bae, Su-Hwan (Dept. of Computer, Soongsil University)
Shin, Yong-Tae (Dept. of Computer Science, Soongsil University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.3, 2021 , pp. 201-207 More about this Journal
Abstract
Deep learning, a type of machine learning, performs learning while changing the weights as it progresses through each learning process. Tensor Flow and Keras provide the results of the end of the learning in graph form. Thus, If an error occurs, the result must be discarded. Consequently, existing technologies provide a function to roll back learning results, but the rollback function is limited to results up to five times. Moreover, they applied the concept of MLOps to track the deep learning process, but no rollback capability is provided. In this paper, we construct a system that manages the intermediate value of the learning process by blockchain to record the intermediate learning process and can rollback in the event of an error. To perform the functions of blockchain, the deep learning process and the rollback of learning results are designed to work by writing Smart Contracts. Performance evaluation shows that, when evaluating the rollback function of the existing deep learning method, the proposed method has a 100% recovery rate, compared to the existing technique, which reduces the recovery rate after 6 times, down to 10% when 50 times. In addition, when using Smart Contract in Ethereum blockchain, it is confirmed that 1.57 million won is continuously consumed per block creation.
Keywords
BlockChain; Configuration Management System; Deep Learning; Ethereum; Tensor Flow;
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