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Add gate on full-sweep / fail-fast runs to prevent wasted compute

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#2,103 0 commenti 0 reazioni 2 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

@adibarra ci sta già lavorando.

Dal 6/7/2026.

Valutazione

Questa issue non è ancora stata valutata.

Descrizione

dev velocity enhancement p1

Summary

Add a gate on compute-heavy sweep runs (full-sweep-fail-fast, full-sweep, non-canary-full-sweep-enabled, etc.) to prevent obviously-wrong / wasteful runs from consuming compute before they start.

Motivation

Compute-intensive sweeps are expensive, and there is currently no guardrail to stop a run that is clearly misconfigured or violating known bad patterns. This wastes runner time, increases queue times, and blocks other engineers' work. Stopping wasted compute here is P1.

Related: this ties into the broader InferenceX compute-improvement effort of stopping people from sweeping trtllm/atom before submitting/validating vllm/sglang, which increases queue time and blocks vllm/sglang work (ATOM engineers wasting other AMD engineers' time).

Approaches discussed

  • FORBIDDEN_HACK.md allow/deny list (preferred starting point): Maintain a simple markdown file of forbidden patterns/hacks that we append to as needed. A gate reads it and denies a run when a known bad pattern is detected. Cheap, deterministic, low false-positive risk.
    • Run this gate only on fail-fast / canary runs (not every run) to limit overhead.
  • LLM-based gate (fallback / later): Call an LLM to evaluate whether a run should be denied.
    • Pros: catches novel bad patterns.
    • Cons: risk of false positives; cost. If pursued, use a cheaper model (e.g. Haiku / Sonnet 5 / fable5) rather than an expensive review model. Estimated ~$1–2 per fable call, cheaper than the ~$20/review Claude enterprise PR code review.

Notes

  • The CODEOWNER sign-off review is considered valuable and should stay.
  • Keep the gate scoped to fail-fast / canary runs initially to control cost.

Scope

Start with the FORBIDDEN_HACK.md + gate on fail-fast/canary runs. Evaluate an LLM-based check later if needed.

Lingua principale
Python
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1.9k
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PR unite (30g)
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