📒 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.

🚨 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

🛡️ 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

📝 How It Works

SemVers Diff
Parses model‑card.json, compares tokenizer_sha, pytorch_sha, cuda, etc., with container runtime; throws if mismatch unless --force.
Boot Checkpoint (shared)
Kubernetes init‑container polls secret store; fails pod after secret_timeout.
Cuda‑Probe
Minimal nvidia‑smi check; if driver ≠ compiled CUDA, WFGY rewrites env CUDA_VISIBLE_DEVICES="" and logs downgrade.
Prompt Lint
CI step: scans prompts for {{ }} or ${} tokens lacking a default in prompt_vars.yaml.

✍️ 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)

🗺️ 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

📊 Implementation Status

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

📝 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.

🔗 Quick‑Start Downloads (60 sec)

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

🧭 Explore More

Module	Description	Link
WFGY Core	Standalone semantic reasoning engine for any LLM	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →

👑 Early Stargazers: See the Hall of Fame —
Engineers, hackers, and open source builders who supported WFGY from day one.

⭐ Help reach 10,000 stars by 2025-09-01 to unlock Engine 2.0 for everyone ⭐ Star WFGY on GitHub

7.2 KiB Raw Blame History Unescape Escape

📒 Problem #16 ·Pre‑Deploy Collapse Problem Map