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Networking Optimizations for Multi-Node Deep Learning on Kubernetes with Erez Cohen - #345

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Release Date: 02/05/2020

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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More Episodes

Today we conclude our KubeCon ‘19 Series joined by Erez Cohen, VP of CloudX & AI at Mellanox. In our conversation, we discuss:

  • Erez’s talk “Networking Optimizations for Multi-Node Deep Learning on Kubernetes.” where he discusses problems and solutions related to networking discovered during the journey to reduce training time. 
  • NVIDIA’s recent acquisition of Mellanox, and what fruits that relationship hopes to bear. 
  • The evolution of technologies like RDMA, GPU Direct, and Sharp, Mellanox’s solution to improve the performance of MPI operations, which can be found in NVIDIA’s NCCL collective communications library.
  • How Mellanox is enabling Kubernetes and other platforms to take advantage of the various technologies mentioned above. 
  • Why we should care about networking in Deep Learning, which is inherently a compute-bound process. 

The complete show notes for this episode can be found at twimlai.com/talk/345.