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1
Introduction
2
Project Background
3
Large Scale Training
4
Elastic Deep Learning
5
Knowledge Distillation
6
Advantages
7
Training Vector
8
William Wang
9
Challenges
10
CNCF Sandbox
11
Volcano Architecture
12
Survival Kubernetes
13
Volcano Job
14
GPU Sharing
15
Cromwell
16
Commander
17
Kubernetes
Description:
Explore a conference talk on optimizing knowledge distillation training using Volcano. Delve into the innovative approach of leveraging Volcano as a scheduler to deploy Teacher models in online Kubernetes GPU inference card clusters, enhancing the throughput of knowledge distillation processes. Learn how this method allows for flexible scheduling, mitigating task failures during peak hours and maximizing the use of cluster resources. Discover the detailed process of optimizing elastic distillation training with Volcano, complete with benchmark data. Gain insights into large-scale training, Elastic Deep Learning, and the advantages of this approach. Examine the Volcano architecture, GPU sharing techniques, and its integration with Kubernetes for efficient model compression and deployment.

Optimizing Knowledge Distillation Training With Volcano

CNCF [Cloud Native Computing Foundation]
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