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IBM Deep Learning Service

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang
IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York
arXiv:1709.05871 [cs.DC], (18 Sep 2017)

@article{bhattacharjee2017deep,

   title={IBM Deep Learning Service},

   author={Bhattacharjee, Bishwaranjan and Boag, Scott and Doshi, Chandani and Dube, Parijat and Herta, Ben and Ishakian, Vatche and Jayaram, K. R. and Khalaf, Rania and Krishna, Avesh and Li, Yu Bo and Muthusamy, Vinod and Puri, Ruchir and Ren, Yufei and Rosenberg, Florian and Seelam, Seetharami R. and Wang, Yandong and Zhang, Jian Ming and Zhang, Li},

   year={2017},

   month={sep},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision. At the same time, the "as-a-Service"-based business model on the cloud is fundamentally transforming the information technology industry. These two trends: deep learning, and "as-a-service" are colliding to give rise to a new business model for cognitive application delivery: deep learning as a service in the cloud. In this paper, we will discuss the details of the software architecture behind IBM’s deep learning as a service (DLaaS). DLaaS provides developers the flexibility to use popular deep learning libraries such as Caffe, Torch and TensorFlow, in the cloud in a scalable and resilient manner with minimal effort. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes. A resource provisioning layer enables flexible job management on heterogeneous resources, such as graphics processing units (GPUs) and central processing units (CPUs), in an infrastructure as a service (IaaS) cloud.
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