Research and Application of A Platform for Artificial Intelligence Computing and Data Services

doi:10.11871/jfdc.issn.2096-742X.2019.02.008

Abstract

Abstract:

[Background] The rapid development of artificial intelligence technology depends on large-scale computing and data resources. Massive computing capacity is an effective guarantee for fast training of deep learning models. Standardized data is an important basis for artificial intelligence algorithms to carry out training processes and accuracy improvements. [Objective]The artificial intelligence computing and data service platform can effectively integrate computing, data and software resources into a unified virtual infrastructure. The platform can provide an integrated working environment for researchers. Besides, it also supports full life-cycle of model design, training and inference. [Methods] Under the circumstance of the convergence of artificial intelligence and high performance computing, we discuss typical usage scenarios, key features, and non-functional requirements related to artificial intelligence platforms. The platform for Artificial Intelligence Computing and Data Application Services at the Chinese Academy of Sciences is introduced. Then the architecture and services of the platform are discussed to address the issues in design and implement of the platform. [Results] By means of Web Services, command lines and online debug tools, the platform can support the rapid creation of artificial intelligence models in an easy-to-use way, process massive training jobs, and enable data access and transfer. [Conclusions] The platform for artificial intelligence and data services can provide an easy-to-use integrated work environment, and further advance the development of artificial intelligence in multiple research areas and disciplines.

Key words: artificial Intelligence platform, computing service, resource service, application service

Wang Yangang,Wang Jue,Cao Rongqiang. Research and Application of A Platform for Artificial Intelligence Computing and Data Services[J]. Frontiers of Data and Computing, 2019, 1(2): 86-97.

Figures/Tables 6

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 27

[1]	Hinton Geoffrey E, Simon Osindero, and Yee-Whye Teh . A fast learning algorithm for deep belief nets[J]. Neural computation 18. 7(2006):1527-1554.
[2]	Rob Farber . 人工智能与高性能计算正在逐步融合[J]. inside HPC intel, 2017, https://www.intel.cn/content/www/cn/zh/high-performance-computing/ai-hpc-is-happening-now-report.html.
[3]	Chen Y, Luo T, Liu S, et al. Dadiannao: A machine-learning supercomputer [C]// Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, 2014: 609-622.
[4]	Cui H, Zhang H, Ganger G R, et al. Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server [C]// Proceedings of the Eleventh European Conference on Computer Systems. ACM, 2016: 4.
[5]	Deng L, Yu D . Deep learning: methods and applications[J]. Foundations and Trends® in Signal Processing, 2014,7(3-4):197-387.
[6]	Najafabadi M M, Villanustre F, Khoshgoftaar T M , et al. Deep learning applications and challenges in big data analytics[J]. Journal of Big Data, 2015,2(1):1.
[7]	Coates A, Huval B, Wang T, et al. Deep learning with COTS HPC systems [C]// International conference on machine learning. 2013: 1337-1345.
[8]	Abadi M, Barham P, Chen J, et al. Tensorflow: A system for large-scale machine learning [C]// 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 2016: 265-283.
[9]	张卫民, 刘灿灿, 骆志刚 . 科学工作流技术研究综述[J]. 国防科技大学学报, 2011,33(3):56-65.
[10]	Dai J, Wang Y, Qiu X , et al. BigDL: A distributed deep learning framework for big data[J]. arXiv preprint arXiv:1804.05839, 2018.
[11]	Castelvecchi D . Particle physicists turn to AI to cope with CERN’s collision deluge[J]. Nature, 2018,557(7706):147-149.
[12]	Zhavoronkov Alex . Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. ( 2018):4311-4313.
[13]	Min S, Lee B, Yoon S . Deep learning in bioinformatics[J]. Briefings in bioinformatics, 2017,18(5):851-869.
[14]	Calegari P, Levrier M, Balczyński P . Web Portals for High-performance Computing: A Survey[J]. ACM Transactions on the Web (TWEB), 2019,13(1):5.
[15]	Zhang J, Lu X, Chakraborty S, et al. Slurm-V: Extending slurm for building efficient HPC cloud with SR-IOV and IVShmem [C]// European Conference on Parallel Processing. Springer, Cham, 2016: 349-362.
[16]	Wall C . Techniques and Architectures for Providing a Command Line Interface Functionality as a Web Service: U.S. Patent Application 15/466,752[P]. 2018-9-27.
[17]	Fielding R T . Architectural styles and the design of network-based software architectures[J]. University Of California, Irvine, 2000.
[18]	Pautasso C, Zimmermann O, Leymann F . Restful web services vs. big’web services: making the right architectural decision [C]// Proceedings of the 17th international conference on World Wide Web. ACM, 2008: 805-814.
[19]	Baer T, Peltz P, Yin J, et al. Integrating apache spark into pbs-based hpc environments [C]// Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure. ACM, 2015: 34.
[20]	Hollowell C, Strecker-Kellogg W, Barnett J, et al. Mixing HTC and HPC workloads with HTCondor and Slurm [C]// J. Phys. Conf. Ser. 2017,898:082014.
[21]	Lee D Y, Jeong K, Han S H, et al. Understanding write behaviors of storage backends in ceph object store [C]// Proceedings of the 2017 IEEE International Conference on Massive Storage Systems and Technology. 2017,10.
[22]	Kluyver T, Ragan-Kelley B, Pérez F, et al. Jupyter Notebooks-a publishing format for reproducible computational workflows [C]// ELPUB. 2016: 87-90.
[23]	Kurtzer G M, Sochat V, Bauer M W . Singularity: Scientific containers for mobility of compute[J]. PloS one, 2017,12(5):e0177459.
[24]	Le E, Paz D . Performance analysis of applications using singularity container on sdsc comet [C]// Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact. ACM, 2017: 66.
[25]	Zonca A, Sinkovits R S . Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops[J]. 2018.
[26]	Strande S M, Cai H, Cooper T, et al. Comet: Tales from the Long Tail: Two Years In and 10,000 Users Later [C]// Practice & Experience in Advanced Research Computing on Sustainability, Success & Impact. 2017.
[27]	Kuznetsov V. Machine Learning as a Service for HEP[J]. 2018. http://export.arxiv.org/pdf/1811.04492.