Hey, I was wondering whether the tech industries require deterministic ML training or is non-determinism acceptable? As in, I know in case of LLMs, small float differences while training are generally tolerated as the model converges to similar quality regardless. But in more specific domains like medical device certification or autonomous vehicle safety validation or financial model auditing, I feel like whether two training runs producing bit identical models would be critical whether two training runs producing bit-identical models is critical, on the same hardware, let alone across heterogeneous hardware. I have read how in certain cases like HFT or Quant, people would just use integers to make deterministic actions, wondering if its the same case here too?