Network hardware faults are inevitable in massive scale-out ML training clusters. Networks in such systems are inherently designed for resiliency, routing around faulty components as long as a fault is detected. Unfortunately, some silent faults evade detection. Notably, the effects of silent faults are amplified in modern production networks that deploy per-packet load balancing, because packets of a single flow traverse many network paths, making such faults particularly hard to localize.