Democratizing Machine Learning on Kubernetes

Democratizing Machine Learning on Kubernetes

Docker via YouTube Direct link

Compute to Communication Ratio

13 of 20

13 of 20

Compute to Communication Ratio

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Democratizing Machine Learning on Kubernetes

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  1. 1 Introduction
  2. 2 Who are we
  3. 3 Why is this important
  4. 4 Data Parallelization
  5. 5 Model Parallelization
  6. 6 Distributed Flow Training
  7. 7 Tensorflow Tools
  8. 8 Demos
  9. 9 Training Environment
  10. 10 Model Performance
  11. 11 Distributed Training
  12. 12 Distributed Training Results
  13. 13 Compute to Communication Ratio
  14. 14 Other Observations
  15. 15 How Can We Improve
  16. 16 Ubers
  17. 17 Cluster Performance
  18. 18 FreeFlow on CNI
  19. 19 GPU Resource Scheduler
  20. 20 Fast AI

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