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Xunan Dai

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https://github.com/baidu-baige/LoongFlow

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replied to their post about 21 hours ago
LoongFlow Big News!!! @all We’ve put AI Agents into a production GPU cluster to handle GPU failure prediction. Not as a demo. Not as AutoML. But as an evolving system that designs and improves its own models. On two GPU types: – IT21HMDB01-B2: +30% prediction accuracy – H800: +25% prediction accuracy The resulting models already meet production standards and are being wired into the ops pipeline. How it works: • An ML agent designs the full ML pipeline from scratch • A Math agent performs targeted evolutionary optimization • The agents explore, discard, and iterate toward better modelsHumans don’t hand-tune parameters. This is not offline analysis. GPU failure prediction means: • heavy assets • real incidents • real operational risk The agents now trigger maintenance before failures happen. This feels like an early signal: AI agents are starting to take responsibility for infrastructure-level engineering decisions in production systems. For ML Agent, you can check: https://github.com/baidu-baige/LoongFlow
replied to their post about 21 hours ago
LoongFlow Big News!!! @all We’ve put AI Agents into a production GPU cluster to handle GPU failure prediction. Not as a demo. Not as AutoML. But as an evolving system that designs and improves its own models. On two GPU types: – IT21HMDB01-B2: +30% prediction accuracy – H800: +25% prediction accuracy The resulting models already meet production standards and are being wired into the ops pipeline. How it works: • An ML agent designs the full ML pipeline from scratch • A Math agent performs targeted evolutionary optimization • The agents explore, discard, and iterate toward better modelsHumans don’t hand-tune parameters. This is not offline analysis. GPU failure prediction means: • heavy assets • real incidents • real operational risk The agents now trigger maintenance before failures happen. This feels like an early signal: AI agents are starting to take responsibility for infrastructure-level engineering decisions in production systems. For ML Agent, you can check: https://github.com/baidu-baige/LoongFlow
replied to their post about 22 hours ago
LoongFlow Big News!!! @all We’ve put AI Agents into a production GPU cluster to handle GPU failure prediction. Not as a demo. Not as AutoML. But as an evolving system that designs and improves its own models. On two GPU types: – IT21HMDB01-B2: +30% prediction accuracy – H800: +25% prediction accuracy The resulting models already meet production standards and are being wired into the ops pipeline. How it works: • An ML agent designs the full ML pipeline from scratch • A Math agent performs targeted evolutionary optimization • The agents explore, discard, and iterate toward better modelsHumans don’t hand-tune parameters. This is not offline analysis. GPU failure prediction means: • heavy assets • real incidents • real operational risk The agents now trigger maintenance before failures happen. This feels like an early signal: AI agents are starting to take responsibility for infrastructure-level engineering decisions in production systems. For ML Agent, you can check: https://github.com/baidu-baige/LoongFlow
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