# Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks
## 5 Conclusions  
As applications increasingly depend on cloud architectures and services, there are negative costs associated with handling excessive I/O operations. These costs are especially harmful to ML-based applications, where a series of training steps can be stalled because of busy I/O scheduling across the storage stacks.  
Figure 6: Total training times for checkpoint methods. (**a**) Total training time for MOD model; (**b**) Total training time for MOT model.  
In this paper, we propose a checkpoint mechanism for ML-based applications called Check-QZP, which is designed to provide a strong incentive by adopting three key ideas: quantization, compression, and parallelism. It should be noted that Check-QZP follows the basic rules for the model deployment and maintenance phases of traditional ML processes; thus, it can be easily reused or managed for other models or systems. In addition, Check-QZP can improve the lifetime of NAND-based storage devices by reducing the number of writes to the devices. For our evaluation, we implemented the prototype of Check-QZP using the PyTorch framework, which is commonly used to realize ML technologies. Our evaluation results clearly confirmed that Check-QZP outperformed the baseline in terms of the elapsed time for checkpoint operations. The reason is that Check-QZP can improve the compression rate through quantization and simultaneously utilize resources on both CPUs and GPUs through parallelism.  
Finally, we believe that our efforts are valuable and can be applied to various environments based on ML frameworks because checkpoints are necessary for running the training step. In particular, we suggest that Check-QZP can have a positive impact on low-end or heterogeneous systems because they commonly suffer from poor I/O performance [54; 55]. Therefore, in the future, we will delve into the performance issues in checkpoint operations and discover additional opportunities for Check-QZP across diverse ML frameworks (i.e., TensorFlow) by analyzing the features of these frameworks. We leave further optimization and detailed studies of Check-QZP's effects on other frameworks for future work.  
Conceptualization, S.L. and D.K.; methodology, S.L.; software, S.L.; validation, S.L. and D.K.; formal analysis, S.L. and D.K.; investigation, S.L. and D.K.; resources, S.L., C.L., H.K. and D.K.; data curation, S.L.; writing--original draft preparation, S.L. and D.K.; writing--review and editing, D.A. and D.K.; visualization, S.L., G.M. and D.K.; supervision, D.A. and D.K.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Gachon University research fund of 2023 (GCU-202400530001) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2023-00251730).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ MOD & Multi-Object Detection \\ MOT & Multi-Object Tracking \\ SSD & Solid State Disk \\ HDD & Hard Disk Drive \\ AI & Artificial Intelligence \\ DL & Deep Learning \\ ML & Machine Learning \\ DNN & Deep Neural Network \\ Check-Q & Checkpoint Quantization \\ Check-Z & Checkpoint Zip \\ Check-P & Checkpoint Parallelism \\ \end{tabular}