~/talk

Reducing Operational Barriers to Model Training

Talkat MLconf NYC 2019 · on Friday, 2019-03-29· [machine-learning, devops-and-delivery]
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Hardware barriers fall but shipping experiments still stalls teams: initial workflow copies files via SCP and TMUX windows, then shifts to building a local image, pushing to a registry and running containers, and finally to Kubernetes on Amazon EKS to run many hyperparameter trials in parallel (six models cuts six days to one). The project builds SigOpt Orchestrate, measures throughput and productivity gains, and exposes a simple CLI that wraps infra APIs with Docker, Kubernetes (Amazon EKS), SigOpt Orchestrate, logs/status CLI.

AI-written summary · about

  1. 0:00Intro
  2. 3:31Operational Barriers
  3. 6:56Can't Train Model on Laptop
  4. 7:48Containerize!
  5. 8:45Start Training in Parallel
  6. 9:45Kubernetes!
  7. 11:49View Progress and Debug
  8. 13:05Build a CLI!
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