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Deep Learning Performance: Scale-up vs Scale-out
Architectures & Technologies Dell EMC | Infrastructure Solutions Group
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9 Citation
@article {sergeev2018horovod,
Author = {Alexander Sergeev and Mike Del Balso},
Journal = {arXiv preprint arXiv: 1802.05799},
Title = {Horovod: fast and easy distributed deep learning in {TensorFlow}},
Year = {2018}
}
10 References
• [1] Nvidia Blogs, “What’s the Difference between Artificial Intelligence, Machine
Learning, and Deep Learning?” [Online]. Available:
https://blogs.Nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-
machine-learning-deep-learning-ai/
• [2] Cornell University Library, “Horovod: fast and easy distributed deep learning in
TensorFlow” [Online]. Available: https://arxiv.org/abs/1802.05799
• [3] Mellanox Community, “How to Create a Docker Container with RDMA Accelerated
Applications Over 100Gb InfiniBand Network” [Online]. Available:
https://community.mellanox.com/docs/DOC-2971
• [4] Horovod GitHub, “Horovod in Docker” [Online]. Available:
https://github.com/uber/horovod/blob/master/docs/docker.md
• [5] Nvidia, ”CUDA Toolkit Documentation” [Online],
https://docs.Nvidia.com/cuda/profiler-users-guide/index.html#profiling-overview
• [6] Cornell University Library, “Training ImageNet in 1 Hour” [Online]. Available:
https://arxiv.org/abs/1706.02677
• [7] Medium, “Hardware for Deep Learning. Part 3: GPU” [Online]. Available:
https://blog.inten.to/hardware-for-deep-learning-part-3-gpu-8906c1644664
• [8] Sergeev, A., Del Balso, M. (2017) Meet Horovod: Uber’s Open Source Distributed Deep
Learning Framework for TensorFlow. Retrieved from https://eng.uber.com/horovod/
• [9] Sergeev, A. (2017) Horovod - Distributed TensorFlow Made Easy. Retrieved from
https://www.slideshare.net/AlexanderSergeev4/horovod-distributed-tensorflow-made-easy
• [10] Sergeev, A., Del Balso, M. (2018) Horovod: fast and easy distributed deep learning in
TensorFlow. arXiv:1802.05799