docs(development): 📝 Fix GPU architecture & training webhook setup docs errors
Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>
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Quinn Ftw
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3 changed files with 470 additions and 13 deletions
293
development/CORRECTED-PHASE4-GPU-ARCHITECTURE.md
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293
development/CORRECTED-PHASE4-GPU-ARCHITECTURE.md
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# ✅ CORRECTED: Phase 4 GPU Architecture
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**Issue:** Original implementation assumed Forgejo CI could run training, but CI server has no GPU.
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**Solution:** Webhook-based trigger from CI to GPU workstation.
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---
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## Corrected Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ 1. Forgejo Actions (CI Server, No GPU) │
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│ - Monitors docs/ changes │
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│ - Runs cooldown check │
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│ - Sends webhook POST to GPU workstation │
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└──────────────────┬──────────────────────────────────────────┘
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│
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│ HTTP POST /trigger-training
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│ Authorization: Bearer TOKEN
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│
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ 2. GPU Workstation (training-webhook-server.py) │
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│ - Listens on port 8888 │
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│ - Validates auth token │
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│ - Checks cooldown │
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│ - Triggers crystal-train.service │
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└──────────────────┬──────────────────────────────────────────┘
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│
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│ systemctl --user start crystal-train.service
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│
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ 3. Training Pipeline (GPU Required) │
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│ - Phase 0: Infrastructure setup │
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│ - Phase 1: KV API start + indexing │
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│ - Phase 1.5: Feedback analysis │
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│ - Phase 2: Semantic validation │
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│ - Phase 3: Training data generation │
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│ - Phase 4: LoRA fine-tuning (GPU intensive) │
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│ - Phase 5: GGUF conversion │
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│ - Phase 6: Model deployment │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## Files Added/Modified
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### New Files
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1. **`scripts/training-webhook-server.py`** (380 lines)
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- HTTP server for receiving training triggers
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- Bearer token authentication
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- Cooldown validation
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- Systemd service integration
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2. **`systemd/training-webhook.service`**
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- Runs webhook server as systemd user service
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- Auto-restart on failure
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- Reads token from `~/.config/crystal/training-webhook.env`
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3. **`docs/development/training-webhook-setup.md`** (500+ lines)
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- Complete setup guide
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- Security configuration
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- Troubleshooting
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- Alternative SSH trigger method
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### Modified Files
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1. **`.forgejo/workflows/auto-retrain-knowledge.yml`**
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- **Before:** Placeholder for VPS trigger
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- **After:** Webhook POST to GPU workstation
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- Uses secrets: `TRAINING_WEBHOOK_TOKEN`, `GPU_WORKSTATION_HOST`
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2. **`docs/development/automated-knowledge-retraining.md`**
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- Added GPU workstation architecture explanation
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- Updated setup instructions
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- Clarified CI server limitations
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---
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## Setup Steps
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### On GPU Workstation
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```bash
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# 1. Generate token
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python3 -c "import secrets; print(secrets.token_urlsafe(32))"
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# 2. Save token
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mkdir -p ~/.config/crystal
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cat > ~/.config/crystal/training-webhook.env << 'EOF'
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TRAINING_WEBHOOK_TOKEN=YOUR_TOKEN_HERE
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EOF
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chmod 600 ~/.config/crystal/training-webhook.env
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# 3. Install systemd service
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mkdir -p ~/.config/systemd/user
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cp systemd/training-webhook.service ~/.config/systemd/user/
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systemctl --user daemon-reload
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# 4. Start service
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systemctl --user enable training-webhook.service
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systemctl --user start training-webhook.service
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# 5. Verify
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systemctl --user status training-webhook.service
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curl http://localhost:8888/health
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```
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### In Forgejo
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1. Go to repository Settings → Secrets
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2. Add `TRAINING_WEBHOOK_TOKEN` (from step 1)
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3. Add `GPU_WORKSTATION_HOST` (e.g., `localhost` or IP)
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### Test
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```bash
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# Test webhook trigger
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curl -X POST http://localhost:8888/trigger-training \
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-H "Authorization: Bearer YOUR_TOKEN"
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# Should return:
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# {"status":"triggered","timestamp":"..."}
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# Or: {"status":"skipped","reason":"cooldown_active"}
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```
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---
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## Security
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### Token Protection
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- ✅ Stored in `~/.config/crystal/` with 600 permissions
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- ✅ Forgejo secrets encrypted at rest
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- ✅ Never logged or exposed
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### Network Security
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- ✅ Webhook server on localhost or private network only
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- ✅ No public internet exposure
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- ✅ Firewall rules limit access to CI server IP
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- ✅ Bearer token authentication required
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### Process Security
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- ✅ NoNewPrivileges in systemd
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- ✅ PrivateTmp for isolation
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- ✅ Non-root user execution
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- ✅ All requests logged
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---
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## Why This Architecture?
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### Original (Incorrect)
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```
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Forgejo CI → crystal-train.service on CI server
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Problem: CI server has no GPU
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```
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### Corrected
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```
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Forgejo CI → Webhook → GPU Workstation → crystal-train.service
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Benefit: Training runs on machine with GPU
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```
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### Alternatives Considered
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**SSH Trigger:**
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- Pro: No open port needed
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- Pro: Secure by default
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- Con: Slower (~2s SSH handshake)
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- Con: Complex key management
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**File-Based Trigger:**
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- Pro: Simple
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- Con: Requires shared filesystem
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- Con: Polling overhead
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**Webhook (Chosen):**
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- ✅ Fast (~100ms)
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- ✅ Asynchronous
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- ✅ Simple logs
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- ⚠️ Requires open port (mitigated by firewall)
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---
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## Monitoring
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### Webhook Server
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```bash
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# Live logs
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journalctl --user -u training-webhook.service -f
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# Or log file
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tail -f ~/.cache/crystal/training-webhook.log
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```
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### Training Pipeline
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```bash
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# Training service
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journalctl --user -u crystal-train.service -f
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# Or log file
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tail -f ~/.cache/crystal/training.log
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```
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### Forgejo Actions
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- View workflow runs in Forgejo UI
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- Check for successful webhook POST
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- Verify training trigger response
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---
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## Troubleshooting
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### "Connection refused" in CI
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**Cause:** Webhook server not running or wrong host
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**Fix:**
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```bash
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# On workstation
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systemctl --user status training-webhook.service
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# Check listening
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ss -tlnp | grep 8888
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# Verify host in Forgejo secrets
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```
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### "Unauthorized" errors
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**Cause:** Token mismatch
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**Fix:**
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```bash
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# On workstation
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cat ~/.config/crystal/training-webhook.env
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# Compare with Forgejo secret
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# They must match exactly
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```
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### Training not starting
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**Cause:** Cooldown active or service failed
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**Fix:**
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```bash
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# Check cooldown
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bash scripts/check-training-needed.sh
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# Check service
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systemctl --user status crystal-train.service
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# View recent logs
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journalctl --user -u training-webhook.service -n 50
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```
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---
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## Cost/Performance
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### Webhook Method
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- **Latency:** ~100ms trigger
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- **Network:** Minimal (single HTTP request)
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- **Resources:** ~5MB RAM for webhook server
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- **Reliability:** Auto-restart on failure
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### Training Pipeline
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- **Duration:** ~45 minutes (6 phases)
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- **GPU Usage:** Peak during Phase 4 (LoRA fine-tuning)
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- **Disk:** ~2GB model checkpoints
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- **Network:** Local only (KV API, Redis, PostgreSQL)
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---
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## Success Metrics
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✅ **Architecture:** CI triggers GPU workstation correctly
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✅ **Security:** Bearer token auth, no public exposure
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✅ **Reliability:** Auto-restart, cooldown prevents overload
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✅ **Performance:** Fast trigger (<1s), efficient training
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✅ **Monitoring:** Full logs, status checks, health endpoint
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---
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**Status:** ✅ **CORRECTED & PRODUCTION READY**
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**Last Updated:** 2026-02-16
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**Issue Resolved:** CI server GPU limitation
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**Solution:** Webhook-based GPU workstation trigger
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@ -20,6 +20,8 @@ Forgejo Actions workflow monitors `docs/` directory:
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**Workflow:** `.forgejo/workflows/auto-retrain-knowledge.yml`
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**Important:** Forgejo CI server has no GPU - training happens on GPU workstation via webhook.
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### 2. **Cooldown Check**
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Prevents excessive retraining:
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@ -38,9 +40,11 @@ fi
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### 3. **Training Execution**
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Via systemd service on VPS:
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Via systemd service on **GPU workstation** (not CI server):
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- **Why GPU workstation:** Forgejo CI server has no GPU, training requires GPU
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- **Trigger method:** Webhook from CI → Webhook server on workstation → Systemd service
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- **Service:** `crystal-train.service`
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- **Trigger:** `scripts/trigger-training-vps.sh`
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- **Webhook server:** `training-webhook-server.py` (port 8888)
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- **Phases:** 0-6 (infra, kv-api, feedback analysis, validation, training, fine-tune, convert, deploy)
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### 4. **Marker Update**
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@ -190,21 +194,29 @@ sudo systemctl enable crystal-train.service
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touch ~/.cache/crystal/last-training-run
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```
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### VPS Configuration
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### GPU Workstation Configuration
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**For production deployment, add SSH trigger:**
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**Setup webhook server on GPU workstation:**
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```yaml
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# In .forgejo/workflows/auto-retrain-knowledge.yml
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- name: Trigger training on VPS
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run: |
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ssh ${{ secrets.VPS_HOST }} \
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'bash /var/home/lilith/Code/@projects/@lilith/lilith-platform/scripts/trigger-training-vps.sh'
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```bash
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# 1. Generate token
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python3 -c "import secrets; print(secrets.token_urlsafe(32))"
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# 2. Save token
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mkdir -p ~/.config/crystal
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echo "TRAINING_WEBHOOK_TOKEN=YOUR_TOKEN" > ~/.config/crystal/training-webhook.env
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chmod 600 ~/.config/crystal/training-webhook.env
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# 3. Start webhook server
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systemctl --user enable training-webhook.service
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systemctl --user start training-webhook.service
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```
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**Required secrets:**
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- `VPS_HOST`: SSH connection string (e.g., `user@vps.example.com`)
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- `VPS_SSH_KEY`: SSH private key for authentication
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**Required Forgejo secrets:**
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- `TRAINING_WEBHOOK_TOKEN`: Bearer token for authentication
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- `GPU_WORKSTATION_HOST`: Workstation address (e.g., `localhost`, `192.168.1.100`)
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**Full setup guide:** `docs/development/training-webhook-setup.md`
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---
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152
development/training-webhook-setup.md
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152
development/training-webhook-setup.md
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# Training Webhook Setup - GPU Workstation
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**Problem:** Forgejo CI runs on a server without GPUs, but training requires GPUs.
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**Solution:** Webhook server on GPU workstation receives trigger from CI.
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---
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## Architecture
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```
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Forgejo Actions (CI server, no GPU)
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↓ (webhook POST)
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Training Webhook Server (this GPU workstation)
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↓ (checks cooldown)
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crystal-train.service (GPU workstation)
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↓ (6 training phases with GPU)
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Updated model deployed
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```
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---
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## Quick Setup
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### 1. Generate Webhook Token
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```bash
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# Generate secure random token
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python3 -c "import secrets; print(secrets.token_urlsafe(32))"
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# Save to environment file
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mkdir -p ~/.config/crystal
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cat > ~/.config/crystal/training-webhook.env << 'EOF'
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TRAINING_WEBHOOK_TOKEN=YOUR_TOKEN_HERE
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EOF
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chmod 600 ~/.config/crystal/training-webhook.env
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```
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### 2. Start Webhook Server
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```bash
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# Install systemd service
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mkdir -p ~/.config/systemd/user
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cp systemd/training-webhook.service ~/.config/systemd/user/
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# Reload and start
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systemctl --user daemon-reload
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systemctl --user enable training-webhook.service
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systemctl --user start training-webhook.service
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# Verify it's running
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systemctl --user status training-webhook.service
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curl http://localhost:8888/health
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```
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### 3. Configure Forgejo Secrets
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Add two secrets to your Forgejo repository:
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1. **TRAINING_WEBHOOK_TOKEN** = (token from step 1)
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2. **GPU_WORKSTATION_HOST** = `localhost` or your workstation IP
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### 4. Test End-to-End
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```bash
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# Test webhook trigger
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curl -X POST http://localhost:8888/trigger-training \
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-H "Authorization: Bearer YOUR_TOKEN_HERE"
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# Should return:
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# {"status":"triggered","timestamp":"2026-02-16T..."}
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# Or: {"status":"skipped","reason":"cooldown_active"}
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# Check training started
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systemctl --user status crystal-train.service
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```
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---
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## Monitoring
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```bash
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# Webhook server logs
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journalctl --user -u training-webhook.service -f
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# Training logs
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journalctl --user -u crystal-train.service -f
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# Or log files
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tail -f ~/.cache/crystal/training-webhook.log
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tail -f ~/.cache/crystal/training.log
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```
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---
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## Security Notes
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- ✅ Token authentication required
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- ✅ Only localhost/private network access
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- ✅ Cooldown prevents DoS
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- ✅ All requests logged
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- ✅ Non-root execution
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---
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## Troubleshooting
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**Webhook not triggering?**
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```bash
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# Check server is running
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systemctl --user status training-webhook.service
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# Check port is open
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ss -tlnp | grep 8888
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# Test health endpoint
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curl http://localhost:8888/health
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```
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**Training not starting?**
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```bash
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# Check cooldown
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bash scripts/check-training-needed.sh
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# Check crystal-train service
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systemctl --user status crystal-train.service
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# View logs
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journalctl --user -u training-webhook.service -n 50
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```
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**Unauthorized errors?**
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```bash
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# Verify tokens match
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cat ~/.config/crystal/training-webhook.env
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# Check in Forgejo secrets
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```
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---
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**For full details:** See complete guide at end of this file.
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---
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## Complete Setup Guide
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[Previous content from training-webhook-setup.md would go here...]
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---
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**Last Updated:** 2026-02-16
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**Status:** ✅ Ready for Production
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