Horovod - Distributed Deep Learning for Reliable MLOps

Horovod - Distributed Deep Learning for Reliable MLOps

Linux Foundation via YouTube Direct link

Intro

1 of 24

1 of 24

Intro

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Horovod - Distributed Deep Learning for Reliable MLOps

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  1. 1 Intro
  2. 2 Early Adoption of Horovod
  3. 3 Deep Learning Refresher
  4. 4 Distributed Deep Learning
  5. 5 Early Distributed Training - Parameter Servers
  6. 6 Parameter Servers - Tradeoffs
  7. 7 Horovod Technique: Allreduce
  8. 8 Benchmarking
  9. 9 Deep Learning in Research
  10. 10 Deep Learning in Production
  11. 11 Feature Store
  12. 12 Model Training
  13. 13 Preprocessing
  14. 14 Spark ML Pipelines
  15. 15 Petastorm: Data Access for Deep Learning Training Challenges of Training on Large Datasets
  16. 16 Spark 3.0: Resource Aware Scheduling
  17. 17 What if my Spark cluster doesn't have GPUs? Horovod Lambda - Run data processing on CPUs with Spark
  18. 18 Online Prediction
  19. 19 Neuropod: Out-of-Process Execution
  20. 20 Workflow Authoring Can we ideate, define, evaluate and deploy a Deep Learning model all within a single script?
  21. 21 Feature Engineering
  22. 22 Model Construction
  23. 23 Model Deployment
  24. 24 Elastic Horovod: Control Flow

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