Incident Library
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  1. Symptoms

    ByteDance's large language model training jobs on its MegaScale system, which runs on more than 10,000 GPUs, occasionally stalled or slowed down. The cause was network interfaces that flapped frequently.

    Trigger

    Network interfaces went down for several seconds and came back up, dropping every packet in transit.

    Root cause
    Component
    Contributing factor
    Detection

    MegaScale's monitoring system uses second-level monitoring to track link flapping and other NIC issues. When the RDMA traffic metrics carried in the training daemons' heartbeats drop sharply or fluctuate abnormally, the driver raises an alert for manual investigation, and if the traffic stops entirely it starts the fault recovery procedure automatically.

    Remediation
    • set NCCL's timeout threshold explicitly to a larger value than the default
    • tuned NCCL's retransmit timer and retry count for fast recovery under link flapping
    • enabled the adap_retrans feature on the NICs
    • applied lower-level quality control to NIC signal strength, AOC cable quality and switch-side signal strength
    Lessons

    A default NCCL timeout can be shorter than the few seconds an interface needs to come back from a flap, so a brief link loss becomes a failed collective and a stalled job. A larger timeout and faster retransmission give the link time to return, but the flapping itself came from physical link quality between NIC, AOC cable and switch, and quality control at that level reduced it.

Check your cluster for this

This case involves NCCL and NIC, cable and switch port link. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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