Neural Pipeline Orchestrator
ProductionA production-grade distributed training orchestrator that handles resource allocation, experiment tracking, and automatic recovery from failures across multi-GPU environments.
Problem
Managing distributed ML training requires complex coordination between nodes, handling failures gracefully, and maintaining experiment reproducibility.
Architecture
Built on a master-worker architecture using Ray for distributed computing. The scheduler optimizes GPU allocation using a custom bin-packing algorithm.
Challenges
Handling partial GPU failures without losing training progress required implementing a novel checkpointing strategy that saves optimizer state every 100 steps.
Lessons Learned
Distributed systems are fundamentally about graceful degradation. The best orchestrator is one that recovers so fast users never notice a failure.