Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 53

Deep Learning Performance: Scale-up vs Scale-out 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-intelligencemachine-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 Architectures & Technologies Dell EMC | Infrastructure Solutions Group 52