Affiliation:
1. University POLITEHNICA of Bucharest , Romania
2. Technical Military Academy “Ferdinand I” Bucharest Romania
Abstract
Abstract
Developing Artificial Intelligence is a labor intensive task. It implies both storage and computational resources. In this paper, we present a state-of-the-art service based infrastructure for deploying, managing and serving computational models alongside their respective data-sets and virtual environments. Our architecture uses key-based values to store specific graphs and datasets into memory for fast deployment and model training, furthermore leveraging the need for manual data reduction in the drafting and retraining stages. To develop the platform, we used clustering and orchestration to set up services and containers that allow deployment within seconds. In this article, we cover high performance computing concepts such as swarming, GPU resource management for model implementation in production environments with emphasis on standardized development to reduce integration tasks and performance optimization.
Reference14 articles.
1. [1] Mo, Y. J., Kim, J., Kim, J.-K., Mohaisen, A. and Lee, W., Performance of deep learning computation with TensorFlow software library in GPU-capable multi-core computing platforms, In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). doi: 10.1109/icufn.2017.7993784.10.1109/icufn.2017.7993784
2. [2] Agile Admin “What is DevOps: https://theagileadmin.com/what-is-devops/ As of 19 May 2018.
3. [3] Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Computer Vision and Pattern Recognition Going Deeper with Convolutions, In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
4. [4] K. Wongsuphasawat et al., Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow, IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 1-12, Jan. 2018.
5. [5] Rutkowski, Leszek, Image classification with recurrent attention models, In: Artificial Intelligence and Soft Computing, 11th International Conference, ICAISC 2012, Zakopane, Poland, April 29 -May 3, 2012: Proceedings. Springer, 2012.
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献