Generative AI Model Data Pre-Training on Kubernetes: A Use Case Study
summary
Demonstrates a production pipeline using Kubeflow Pipelines (KFP), Ray via KubeRay, and the open Data Prep Kit (DPK) to preprocess PetaByte-scale LLM data with reproducible, nested pipelines and per-step Ray clusters. A fuzzy deduplication run on 250,000 parquet files (23 TB compressed, ~8.5 billion docs) reduces to 5.6 billion documents and ~14 TB stored, running on 7,500 CPU cores and 56 TB RAM over 40 hours; checkpointing and exit handlers prevent losing days of work.
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chapters (9)
- 0:00Introduction
- 1:03Data processing workflows
- 3:33Scaling data prep with Ray on K8s
- 6:45Reproducibility with Kubeflow Pipelines
- 10:18Case study: data pre-processing pipeline
- 13:29Live demo: super pipeline run
- 16:17Results: largest data prep job
- 17:41Consistent development with Data Prep Kit
- 20:58Q&A
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