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Infrastructure2026

WARDEN

Catching silent corruption in distributed GPU training

Real-time silent-error detection, weight-integrity monitoring and automatic recovery for PyTorch distributed training jobs.

//The problem

A long training run that silently corrupts partway through costs the entire compute budget to discover — usually at evaluation, days later.

//What it does

  • Continuous weight-integrity monitoring during training.
  • Real-time detection of silent errors rather than post-hoc validation.
  • Automatic recovery from detected failures.
  • Three-line integration into an existing training script.

//Measured

Integration
3 lines of code
Overhead
<1%

//Built with

  • Rust
  • PyTorch
  • Distributed training

status: Active