New SCOUT framework detects failures in LLM pre-training

Researchers have developed SCOUT, a new framework designed to pinpoint failures during large language model (LLM) pre-training. SCOUT operates by establishing a strict majority consensus among equivalent replicas to identify outliers, thereby localizing the source of stalls, slowdowns, or numerical errors. This system remains responsive even when the training process hangs, and its replay functionality helps detect silent data corruption and verify checkpoint integrity. SCOUT is compatible with popular frameworks like PyTorch, TorchTitan, Megatron-Core, and DeepSpeed. AI

IMPACT Improves efficiency and reliability of LLM pre-training by enabling faster and more accurate failure diagnosis.

RANK_REASON The cluster describes a new framework presented in an arXiv paper for failure localization in LLM pre-training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Consensus Collective Communication
• DeepSpeed
• LLM
• Megatron-Core
• PyTorch
• SCOUT
• TorchTitan

• infra
• paper

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