Post
7
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
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
