📒 Problem #16 ·Pre‑Deploy Collapse Problem Map

“Everything looked fine in CI… until nothing booted in prod.”

Pre‑deploy collapse happens before a single user query is served.
Migrations pass, tests are green, images ship — yet the first container crashes or the very first LLM call returns a 500. Common root causes:

• Model checkpoint ≠ tokenizer version
• Env var misspells (e.g., OPEN_API_KEY vs OPENAI_API_KEY)
• Hidden f‑strings / Jinja placeholders left unresolved
• GPU drivers mismatch container base (CUDA 11 vs 12)

WFGY’s pre‑flight sanity layer runs a semantic diff between declared runtime and effective runtime, catching mismaps before traffic starts.

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🚨 Typical Pre‑Deploy Collapses

Pattern	Real‑World Fallout
Tokenizer / checkpoint version skew	Embeds garbage; queries 0 % recall
Missing secret in K‑V store	First API call 401 / segfault
CUDA / driver mismatch	GPU container exits code 139
Undigested template vars ({{ }})	Prompt crashes, empty completions

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🛡️ WFGY Pre‑Flight Guards

Collapse Pattern	Guard Module	Remedy	Status
Version skew	SemVers Diff	Abort deploy if model.json ↔ runtime mismatch	✅ Stable
Missing secret	Boot Checkpoint	Block start until secret present	✅ Stable
Driver mismatch	Cuda‑Probe	Warn & fall back to CPU safe mode	⚠️ Beta
Stray {{var}} tokens	Prompt Lint	Fail CI; highlight undeclared vars	✅ Stable

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📝 How It Works

1. SemVers Diff
   Parses model‑card.json, compares tokenizer_sha, pytorch_sha, cuda, etc., with container runtime; throws if mismatch unless --force.

2. Boot Checkpoint (shared)
   Kubernetes init‑container polls secret store; fails pod after secret_timeout.

3. Cuda‑Probe
   Minimal nvidia‑smi check; if driver ≠ compiled CUDA, WFGY rewrites env CUDA_VISIBLE_DEVICES="" and logs downgrade.

4. Prompt Lint
   CI step: scans prompts for {{ }} or ${} tokens lacking a default in prompt_vars.yaml.

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✍️ Demo — Tokenizer Version Skew

$ wgfy preflight
✔ env vars ............... OK
✖ checkpoint ↔ tokenizer .. MISMATCH
  • model: facebook/llama‑2‑7b‑chat‑hf  tokenizer‑sha = `ad4c1b9`
  • runtime: tokenizer‑sha = `9e7f02d`
  → Aborting deploy (use --force to override)

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🗺️ Module Cheat‑Sheet

Module	Role
SemVers Diff	Catch model / tokenizer skew
Boot Checkpoint	Ensure secrets & config exist
Cuda‑Probe	Verify GPU driver compatibility
Prompt Lint	Fail CI on stray template vars

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📊 Implementation Status

Feature	State
SemVers diff	✅ Stable
Boot checkpoint	✅ Stable
Cuda‑probe fallback	⚠️ Beta
Prompt lint in CI action	✅ Stable

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📝 Tips & Limits

• Add ignore_versions: ["minor"] in wgfy.yaml to allow 1‑patch drifts.
• Set secret_timeout = 90s for slower vaults.
• GPU fallback adds ~0.4 s latency per request — tune cuda_probe.mode.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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