How do we build a scalable pretraining workflow?
Effective pretraining pipelines are the backbone of modern AI.
How It Works:
Set up distributed data ingestion, sharded storage, and parallel training across GPUs/TPUs; automate logging and model checkpointing.
Key Benefits:
- Massive scale: Handle terabytes of data.
- Resilience: Recover from node failures seamlessly.
- Reproducibility: Track configs and data versions.
Real-World Use Cases:
- Language models: Scale to hundreds of gigabytes of text.
- Generative vision: Pretrain on web-scale image collections.
FAQs
What infrastructure is needed?
How manage costs?
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