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dongzhuoyao
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sue-dryrun

Validates ML/HPC scale-up experiment readiness via minimal dry runs, catching script, environment and GaPath failures before full launches.

Updated Aug 27, 2026
Search Tags:#dryrun#ml-experiment#scale-up-experiment#wandb-tracking#sandbox-validation#gpu-packing#inode-safety
One-click install
npx skills add https://github.com/dongzhuoyao/deepresearch --skill sue-dryrun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sue-dryrun
Source: https://github.com/dongzhuoyao/deepresearch/tree/main/.codex/skills/sue-dryrun
Command: npx skills add https://github.com/dongzhuoyao/deepresearch --skill sue-dryrun

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the risk of wasting expensive GPU compute on full ML/HPC scale-up experiments that fail due to uncaught script errors, environment mismatches, or path non-compliance, by running a minimal representative dry run to validate all critical paths before full launch.

Core Features & Use Cases

  • Plan-only matrix validation: Enumerates the full experiment matrix, counts, uniqueness, and launch commands without executing every variant, catching configuration errors early.
  • Minimal runtime coverage: Runs a small set of representative real cases covering all distinct evaluator, data-loader, asset, scheduler, tracking, and output paths that could fail independently in the full run.
  • Runtime and makespan estimation: Measures dryrun throughput to estimate total full-run runtime and sandbox completion time under maximum GPU quota and job concurrency.
  • Use Case: For a 500+ variant image classification scale-up experiment, use this skill to run 5-10 representative dry runs to confirm all code and data paths work, estimate total runtime, and catch LUMI container environment mismatches before launching the full expensive grid.

Quick Start

Use the sue-dryrun skill to run a minimal readiness dry run for your DeepResearch scale-up experiment after your launch scripts are written and audited.

Frequently Asked Questions about sue-dryrun

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate ML scale-up experiment readiness before launching full runs?▼

To estimate total runtime for your ML experiment, measure dry run throughput to estimate total full-run runtime and sandbox completion time under maximum GPU quota and job concurrency. This provides a reliable makespan projection before full launch.

Can I run dry run validation on HPC sandboxes like LUMI, Snellius, and AutoDL?▼

A dry run prevents GPU compute waste on full ML scale-up experiments by executing a minimal representative run to catch uncaught script errors, environment mismatches, and path non-compliance early. It validates all critical paths before the full launch.

Does dry run validation support W&B tracking and inode-safe output handling?▼

Yes, dry run validation supports W&B tracking and inode-safe output handling. It satisfies requirements for GPU-vacancy packing, full-path output compliance, W&B/CSV ledger tracking, and repeatable launch posture for full-run readiness.

What is plan-only matrix validation for ML experiments?▼

Plan-only matrix validation enumerates the full ML experiment matrix, counts variants, checks uniqueness, and generates launch commands without executing every variant. This catches configuration errors early before any compute is spent.

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