mirror of
https://github.com/Memo-2023/mana-monorepo.git
synced 2026-05-14 19:21:10 +02:00
chore: major cleanup of legacy docs, reports, and unused configs
Deleted 50 files (~26,000 lines): Root-level legacy reports: - AUTH_*.md (5 files) - auth architecture reports, now in CLAUDE.md - TESTING_STRATEGY_*.md, QA_*, TEST_CASES_*.md - old testing plans - BACKEND_DESIGN_PATTERN_AUDIT.md, COMPATIBILITY_MATRIX_AND_REMEDIATION.md - HISTORICAL-ANALYSIS.md, MERGE-FIX-SUMMARY.md, RELEASE-PLAN.md - MANACORE-TODOS.md, APP-IDEAS.md, COMMANDS.md docs/ cleanup: - 6 testing docs (duplicates/superseded by .claude/guidelines/testing.md) - 3 env audit files (canonical: ENVIRONMENT_VARIABLES.md) - 3 Mac Mini setup docs (canonical: MAC_MINI_SERVER.md) - 5 daily reports (historical, no ongoing value) - SELF-HOSTING-GUIDE.md (Coolify/Hetzner based, obsolete) - CHANGELOG, CONSISTENCY_REPORT, CONSOLIDATION_OPPORTUNITIES, pr-reviews/ .claude/ cleanup: - audit/ directory (Dec 2025 audit, outdated) - Speculative plans (MacBook Pro server, Windows GPU server) Other: - docker-compose.yml (Traefik-based, replaced by docker-compose.macmini.yml) - TROUBLESHOOTING.md trimmed (removed 730-line staging deployment section) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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# Implementierungsplan: MacBook Pro M1 Max als zweiter Server
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## Übersicht
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**Ziel:** MacBook Pro M1 Max (64GB RAM) als AI/ML-Server einrichten, der parallel zum Mac Mini läuft.
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**Zeitschätzung:** 4-6 Stunden für komplette Implementierung
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---
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## Phase 1: Hardware-Vorbereitung (30 Min)
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### 1.1 MacBook Pro physisch vorbereiten
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- [ ] Daten sichern (falls noch nicht geschehen)
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- [ ] Vertikalen Laptop-Ständer besorgen (~25€)
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- [ ] USB-C zu Ethernet Adapter besorgen (~30€)
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- [ ] Stromleiste mit Überspannungsschutz (~30€)
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- [ ] Position neben Mac Mini festlegen
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### 1.2 Netzwerk-Konfiguration planen
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```
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Mac Mini: 192.168.x.10 (bestehend)
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MacBook Pro: 192.168.x.11 (neu)
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```
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---
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## Phase 2: macOS Setup auf MacBook Pro (1-2 Std)
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### 2.1 Optionaler Clean Install
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```bash
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# Falls gewünscht: macOS neu installieren
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# Recovery Mode: Cmd+R beim Start
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# Festplattendienstprogramm → Löschen → APFS
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# macOS neu installieren
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```
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### 2.2 Grundlegende Konfiguration
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```bash
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# Systemeinstellungen
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# 1. Computername setzen
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sudo scutil --set ComputerName "mana-server-ai"
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sudo scutil --set HostName "mana-server-ai"
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sudo scutil --set LocalHostName "mana-server-ai"
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# 2. SSH aktivieren
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# System Settings → General → Sharing → Remote Login → ON
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# 3. Clamshell-Modus ermöglichen
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# System Settings → Battery → Power Adapter:
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# - "Prevent automatic sleeping when the display is off" → ON
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# - "Wake for network access" → ON
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# 4. Auto-Login (optional, für Server-Betrieb)
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# System Settings → Users & Groups → Automatic Login
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# 5. Autostart nach Stromausfall
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sudo systemsetup -setrestartfreeze on
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sudo systemsetup -setrestartpowerfailure on
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```
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### 2.3 Statische IP konfigurieren
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```bash
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# System Settings → Network → Ethernet → Details → TCP/IP
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# Configure IPv4: Manually
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# IP Address: 192.168.x.11
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# Subnet Mask: 255.255.255.0
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# Router: 192.168.x.1
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# DNS: 1.1.1.1, 8.8.8.8
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```
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### 2.4 Development Tools installieren
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```bash
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# Xcode Command Line Tools
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xcode-select --install
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# Homebrew
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/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
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# Essentials
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brew install git node pnpm python@3.11 cloudflared
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# Docker Desktop
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brew install --cask docker
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# Nach Installation: Docker Desktop öffnen und starten
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```
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---
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## Phase 3: Externe SSD einrichten (30 Min)
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### 3.1 Verzeichnisstruktur erstellen
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```bash
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# SSD mounten (falls nicht automatisch)
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# Erwarteter Mount-Punkt: /Volumes/ManaData-AI oder ähnlich
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# Verzeichnisse erstellen
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sudo mkdir -p /Volumes/ManaData-AI/{
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ollama,
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flux2,
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stt-models,
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tts-models,
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postgres-replica,
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backups
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}
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# Berechtigungen setzen
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sudo chown -R $(whoami):staff /Volumes/ManaData-AI
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```
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### 3.2 Symlinks einrichten
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```bash
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# Ollama Modelle
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ln -sf /Volumes/ManaData-AI/ollama ~/.ollama
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# STT Modelle
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ln -sf /Volumes/ManaData-AI/stt-models ~/stt-models
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# TTS Modelle
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ln -sf /Volumes/ManaData-AI/tts-models ~/tts-models
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# FLUX.2 Modelle
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ln -sf /Volumes/ManaData-AI/flux2 ~/flux2
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```
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|
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---
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## Phase 4: Cloudflare Tunnel einrichten (30 Min)
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### 4.1 Neuen Tunnel erstellen
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```bash
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# Bei Cloudflare anmelden
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cloudflared tunnel login
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# Neuen Tunnel für MacBook Pro erstellen
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cloudflared tunnel create mana-server-ai
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# Tunnel-ID notieren (z.B. abc12345-xxxx-xxxx-xxxx-xxxxxxxxxxxx)
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export TUNNEL_ID="<tunnel-id-hier>"
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# DNS-Routen erstellen
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cloudflared tunnel route dns mana-server-ai mbp.mana.how
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cloudflared tunnel route dns mana-server-ai llm.mana.how
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cloudflared tunnel route dns mana-server-ai tts-v2.mana.how
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cloudflared tunnel route dns mana-server-ai stt-v2.mana.how
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cloudflared tunnel route dns mana-server-ai img.mana.how
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```
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### 4.2 Dateien zu erstellen
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**Datei:** `cloudflared-config.macbookpro.yml`
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```yaml
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tunnel: <TUNNEL_ID>
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credentials-file: /Users/mana/.cloudflared/<TUNNEL_ID>.json
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ingress:
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# SSH Access
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- hostname: mbp.mana.how
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service: ssh://localhost:22
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# LLM Service (mana-llm mit Ollama)
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- hostname: llm.mana.how
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service: http://localhost:3025
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originRequest:
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connectTimeout: 300s
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# TTS Service (Kokoro + F5)
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- hostname: tts-v2.mana.how
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service: http://localhost:3022
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# STT Service (Whisper Large)
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- hostname: stt-v2.mana.how
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service: http://localhost:3021
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# Image Generation (FLUX.2)
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- hostname: img.mana.how
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service: http://localhost:3023
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# Catch-all
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- service: http_status:404
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```
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---
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## Phase 5: AI/ML Services installieren (2 Std)
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### 5.1 Ollama mit großen Modellen
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```bash
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# Ollama installieren
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brew install ollama
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# Service starten
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brew services start ollama
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# Große Modelle laden (dauert je nach Verbindung)
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ollama pull gemma3:27b # 16 GB - Hauptmodell
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ollama pull llama3.1:70b # ~40 GB 4-bit quant (optional)
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ollama pull codestral:22b # ~14 GB - Code
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ollama pull deepseek-coder:33b # ~20 GB - Code (optional)
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# Existierende kleinere Modelle auch laden für Kompatibilität
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ollama pull gemma3:4b
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ollama pull gemma3:12b
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```
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### 5.2 mana-tts mit F5-TTS
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```bash
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# Python Virtual Environment
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python3.11 -m venv ~/venvs/mana-tts
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source ~/venvs/mana-tts/bin/activate
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# TTS Dependencies (inkl. F5-TTS für Voice Cloning)
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pip install kokoro-onnx f5-tts torch torchaudio
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pip install fastapi uvicorn python-multipart
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# Modelle herunterladen
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# (Details in services/mana-tts/setup.py oder setup-tts.sh)
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```
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**LaunchAgent erstellen:** `com.manacore.mana-tts.plist`
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### 5.3 mana-stt mit Whisper Large
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```bash
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# Python Virtual Environment
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python3.11 -m venv ~/venvs/mana-stt
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source ~/venvs/mana-stt/bin/activate
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# Whisper installieren
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pip install openai-whisper faster-whisper
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pip install fastapi uvicorn python-multipart
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# Large-v3 Modell herunterladen (wird automatisch geladen)
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# ~3 GB Download
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```
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**LaunchAgent erstellen:** `com.manacore.mana-stt.plist`
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### 5.4 mana-image-gen mit FLUX.2
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```bash
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# Bestehende Setup-Skript verwenden (angepasst)
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./scripts/mac-mini/setup-image-gen.sh
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# Oder manuell:
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cd ~/
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git clone https://github.com/city96/flux2.c
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cd flux2.c
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make MPS=1 # Apple Metal Support
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# Modell herunterladen (~16 GB)
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# Details in services/mana-image-gen/
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```
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**LaunchAgent erstellen:** `com.manacore.image-gen.plist`
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---
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## Phase 6: Docker Services (Optional, für Replicas) (1 Std)
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### 6.1 docker-compose.macbookpro.yml erstellen
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Nur für:
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- PostgreSQL Replica (Hot Standby)
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- Redis Replica
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- mana-llm Container
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- Backup Worker
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### 6.2 PostgreSQL Streaming Replication
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**Auf Mac Mini (Primary):**
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```bash
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# postgresql.conf anpassen
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wal_level = replica
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max_wal_senders = 3
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wal_keep_size = 64MB
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|
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# pg_hba.conf anpassen
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host replication replicator 192.168.x.11/32 md5
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```
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**Auf MacBook Pro (Replica):**
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|
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```bash
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# Base Backup vom Primary
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pg_basebackup -h 192.168.x.10 -U replicator -D /Volumes/ManaData-AI/postgres-replica -P
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# standby.signal erstellen
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touch /Volumes/ManaData-AI/postgres-replica/standby.signal
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# postgresql.auto.conf
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primary_conninfo = 'host=192.168.x.10 port=5432 user=replicator password=xxx'
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```
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---
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## Phase 7: Autostart & Health Checks (30 Min)
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### 7.1 Scripts zu erstellen
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|
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```
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scripts/macbook-pro/
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├── setup-autostart.sh # LaunchAgents einrichten
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├── startup.sh # Boot-Startup
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├── health-check.sh # Service-Monitoring
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├── status.sh # Übersicht
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├── restart.sh # Services neustarten
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└── stop.sh # Services stoppen
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```
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### 7.2 LaunchAgents zu erstellen
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```
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~/Library/LaunchAgents/
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├── com.cloudflare.cloudflared.plist # Tunnel
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├── com.manacore.mana-tts.plist # TTS Service
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├── com.manacore.mana-stt.plist # STT Service
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├── com.manacore.image-gen.plist # Image Gen
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├── com.manacore.health-check.plist # Health Checks
|
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└── homebrew.mxcl.ollama.plist # Ollama (auto von brew)
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```
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|
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---
|
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## Phase 8: Dokumentation & Testing (30 Min)
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|
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### 8.1 Dokumentation aktualisieren
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|
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**Dateien zu erstellen/aktualisieren:**
|
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|
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- `docs/MACBOOK_PRO_SERVER.md` - Neue Dokumentation
|
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- `docs/MAC_MINI_SERVER.md` - Verweise auf MBP hinzufügen
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- `docs/TWO_SERVER_ARCHITECTURE.md` - Architektur-Übersicht
|
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- `CLAUDE.md` - SSH-Config für mbp hinzufügen
|
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|
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### 8.2 SSH-Config erweitern
|
||||
|
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```
|
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# ~/.ssh/config
|
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Host mana-server
|
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HostName mac-mini.mana.how
|
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User till
|
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ProxyCommand /opt/homebrew/bin/cloudflared access ssh --hostname %h
|
||||
|
||||
Host mana-server-ai
|
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HostName mbp.mana.how
|
||||
User till
|
||||
ProxyCommand /opt/homebrew/bin/cloudflared access ssh --hostname %h
|
||||
```
|
||||
|
||||
### 8.3 Testing Checklist
|
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|
||||
- [ ] SSH zu MacBook Pro funktioniert: `ssh mana-server-ai`
|
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- [ ] Ollama API erreichbar: `curl http://192.168.x.11:11434/api/version`
|
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- [ ] TTS Service: `curl http://192.168.x.11:3022/health`
|
||||
- [ ] STT Service: `curl http://192.168.x.11:3021/health`
|
||||
- [ ] Image Gen: `curl http://192.168.x.11:3023/health`
|
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- [ ] LLM Service: `curl https://llm.mana.how/health`
|
||||
- [ ] PostgreSQL Replica synchronisiert
|
||||
- [ ] Health Checks laufen alle 5 Min
|
||||
- [ ] Notifications bei Fehlern
|
||||
|
||||
---
|
||||
|
||||
## Dateien die erstellt werden müssen
|
||||
|
||||
### Neue Dateien
|
||||
|
||||
| Datei | Beschreibung |
|
||||
|-------|--------------|
|
||||
| `docker-compose.macbookpro.yml` | Docker Compose für MBP (Replicas, mana-llm) |
|
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| `cloudflared-config.macbookpro.yml` | Cloudflare Tunnel Config |
|
||||
| `.env.macbookpro` | Environment Variables |
|
||||
| `scripts/macbook-pro/setup-autostart.sh` | LaunchAgent Setup |
|
||||
| `scripts/macbook-pro/startup.sh` | Boot Startup Script |
|
||||
| `scripts/macbook-pro/health-check.sh` | Health Monitoring |
|
||||
| `scripts/macbook-pro/status.sh` | Service Status |
|
||||
| `scripts/macbook-pro/restart.sh` | Restart Services |
|
||||
| `scripts/macbook-pro/stop.sh` | Stop Services |
|
||||
| `scripts/macbook-pro/setup-ollama.sh` | Ollama Setup mit großen Modellen |
|
||||
| `scripts/macbook-pro/setup-tts.sh` | TTS Setup mit F5 |
|
||||
| `scripts/macbook-pro/setup-stt.sh` | STT Setup mit Whisper Large |
|
||||
| `scripts/macbook-pro/backup-worker.sh` | Backup vom Mac Mini |
|
||||
| `docker/postgres/replica-setup.sh` | PostgreSQL Replica Init |
|
||||
| `docs/MACBOOK_PRO_SERVER.md` | Server Dokumentation |
|
||||
| `docs/TWO_SERVER_ARCHITECTURE.md` | Architektur Übersicht |
|
||||
|
||||
### Zu aktualisierende Dateien
|
||||
|
||||
| Datei | Änderung |
|
||||
|-------|----------|
|
||||
| `CLAUDE.md` | SSH-Config für MBP |
|
||||
| `docs/MAC_MINI_SERVER.md` | Verweise auf MBP |
|
||||
| `.env.development` | MBP-spezifische Vars |
|
||||
|
||||
---
|
||||
|
||||
## Architektur nach Implementierung
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ Cloudflare Tunnel │
|
||||
│ *.mana.how → Mac Mini (Primary) │
|
||||
│ llm/tts-v2/stt-v2/img.mana.how │
|
||||
│ → MacBook Pro (AI/ML) │
|
||||
└───────────────┬─────────────────────┘
|
||||
│
|
||||
┌─────────────────────┴─────────────────────┐
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────────────┐ ┌─────────────────────────────┐
|
||||
│ MAC MINI M4 (16GB) │ │ MACBOOK PRO M1 MAX (64GB) │
|
||||
│ "Docker Orchestrator" │ │ "AI/ML Powerhouse" │
|
||||
│ 192.168.x.10 │ │ 192.168.x.11 │
|
||||
├─────────────────────────────┤ ├─────────────────────────────┤
|
||||
│ │ │ │
|
||||
│ PostgreSQL PRIMARY ─────────┼────────▶│ PostgreSQL REPLICA │
|
||||
│ Redis PRIMARY ──────────────┼────────▶│ Redis REPLICA │
|
||||
│ MinIO S3 │ │ │
|
||||
│ │ │ Ollama (27B, 70B Modelle) │
|
||||
│ mana-core-auth (Primary) │ │ mana-llm (large models) │
|
||||
│ API Gateway │ │ │
|
||||
│ mana-search + SearXNG │ │ mana-tts (Kokoro + F5) │
|
||||
│ mana-media │ │ mana-stt (Whisper Large) │
|
||||
│ │ │ mana-image-gen (2048x2048) │
|
||||
│ Alle NestJS Backends │ │ │
|
||||
│ Alle SvelteKit Frontends │ │ Backup-Worker │
|
||||
│ Matrix Synapse + Bots │ │ │
|
||||
│ Monitoring Stack │ │ │
|
||||
│ n8n, Umami │ │ │
|
||||
└─────────────────────────────┘ └─────────────────────────────┘
|
||||
ssh.mana.how mbp.mana.how
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Risiken & Mitigationen
|
||||
|
||||
| Risiko | Mitigation |
|
||||
|--------|------------|
|
||||
| MacBook-Akku bläht sich auf | Monatliche visuelle Prüfung; Al Dente App für Ladelimit bei 80% |
|
||||
| Clamshell Überhitzung | Vertikaler Ständer für Konvektion; Monitoring der Temperatur |
|
||||
| Replication Lag | Monitoring in Grafana; Alerts bei > 1 Minute Lag |
|
||||
| Komplexität | Gute Dokumentation; Health Checks mit Alerts |
|
||||
| macOS Update bricht Services | Auto-Updates deaktivieren; manuelles Update nach Testing |
|
||||
|
||||
---
|
||||
|
||||
## Implementierungs-Reihenfolge
|
||||
|
||||
1. **Hardware vorbereiten** (Phase 1)
|
||||
2. **macOS konfigurieren** (Phase 2)
|
||||
3. **Externe SSD einrichten** (Phase 3)
|
||||
4. **Cloudflare Tunnel** (Phase 4)
|
||||
5. **Ollama + große Modelle** (Phase 5.1)
|
||||
6. **mana-tts migrieren** (Phase 5.2)
|
||||
7. **mana-stt migrieren** (Phase 5.3)
|
||||
8. **mana-image-gen migrieren** (Phase 5.4)
|
||||
9. **Autostart einrichten** (Phase 7)
|
||||
10. **Testing** (Phase 8)
|
||||
11. **PostgreSQL Replication** (Phase 6) - Optional, später
|
||||
12. **Auth Redundanz** - Optional, später
|
||||
|
||||
---
|
||||
|
||||
## Nächste Schritte
|
||||
|
||||
Wenn du bereit bist zu implementieren, sag mir welche Phase wir zuerst angehen sollen. Ich kann dann:
|
||||
|
||||
1. Die entsprechenden Scripts erstellen
|
||||
2. Die Config-Dateien generieren
|
||||
3. Schritt-für-Schritt Anleitung geben
|
||||
|
||||
**Empfehlung:** Starte mit Phase 4 (Cloudflare Tunnel) und Phase 5.1 (Ollama), da diese den größten unmittelbaren Nutzen bringen.
|
||||
|
|
@ -1,500 +0,0 @@
|
|||
# Implementierungsplan: Windows GPU-Server als AI/ML-Rechner (Stufe 1)
|
||||
|
||||
## Übersicht
|
||||
|
||||
**Ziel:** Windows-PC mit starker GPU als dedizierter AI/ML-Server einrichten.
|
||||
Der Mac Mini bleibt Orchestrator für Web-Apps, Backends und Datenbanken.
|
||||
Der Windows-PC übernimmt alle GPU-intensiven AI/ML-Workloads.
|
||||
|
||||
**Architektur:**
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ Cloudflare Tunnels │
|
||||
│ *.mana.how → Mac Mini (Primary) │
|
||||
│ llm/tts/stt/img.mana.how │
|
||||
│ → Windows PC (AI/ML) │
|
||||
└───────────────┬─────────────────────┘
|
||||
│
|
||||
┌─────────────────────┴─────────────────────┐
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────────────┐ ┌─────────────────────────────┐
|
||||
│ MAC MINI M4 (16GB) │ │ WINDOWS GPU-SERVER │
|
||||
│ "Orchestrator" │ LAN │ "AI/ML Powerhouse" │
|
||||
│ 192.168.x.10 │◄───────►│ 192.168.x.11 │
|
||||
├─────────────────────────────┤ ├─────────────────────────────┤
|
||||
│ │ │ │
|
||||
│ PostgreSQL, Redis, MinIO │ │ Ollama + CUDA │
|
||||
│ mana-core-auth │ │ gemma3:27b, llama3.1:70b │
|
||||
│ Alle NestJS Backends │ │ codestral:22b │
|
||||
│ Alle SvelteKit Frontends │ │ │
|
||||
│ Matrix Synapse + Bots │ │ mana-stt (Whisper Large) │
|
||||
│ Monitoring Stack │ │ mana-tts (Kokoro + Piper) │
|
||||
│ n8n, Umami │ │ mana-image-gen (FLUX.2) │
|
||||
│ mana-llm (Gateway) │ │ │
|
||||
│ │ │ Cloudflare Tunnel │
|
||||
│ Ollama gemma3:4b (Fallback) │ │ │
|
||||
└─────────────────────────────┘ └─────────────────────────────┘
|
||||
```
|
||||
|
||||
**Was sich ändert:** Die AI-Services (Ollama, STT, TTS, Image Gen) laufen auf dem Windows-PC statt nativ auf dem Mac Mini. Der Mac Mini behält Ollama mit kleinen Modellen als Fallback. `mana-llm` (der LLM-Gateway-Container) bleibt auf dem Mac Mini, zeigt aber auf den Windows-PC.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Windows-PC vorbereiten (1 Std)
|
||||
|
||||
### 1.1 Voraussetzungen prüfen
|
||||
|
||||
- [ ] Windows 10/11 Pro (für WSL2 + Hyper-V)
|
||||
- [ ] NVIDIA GPU mit aktuellem Treiber (>= 535.x für CUDA 12)
|
||||
- [ ] Mindestens 32GB RAM empfohlen
|
||||
- [ ] Mindestens 200GB freier Speicher für Modelle
|
||||
- [ ] Ethernet-Verbindung zum selben Netzwerk wie Mac Mini
|
||||
|
||||
### 1.2 Statische IP konfigurieren
|
||||
|
||||
```
|
||||
Einstellungen → Netzwerk → Ethernet → IP-Einstellungen bearbeiten
|
||||
IP-Adresse: 192.168.x.11
|
||||
Subnetzmaske: 255.255.255.0
|
||||
Gateway: 192.168.x.1
|
||||
DNS: 1.1.1.1, 8.8.8.8
|
||||
```
|
||||
|
||||
### 1.3 Computername setzen
|
||||
|
||||
```powershell
|
||||
# PowerShell als Admin
|
||||
Rename-Computer -NewName "mana-server-gpu"
|
||||
Restart-Computer
|
||||
```
|
||||
|
||||
### 1.4 SSH aktivieren
|
||||
|
||||
```powershell
|
||||
# PowerShell als Admin
|
||||
Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0
|
||||
Start-Service sshd
|
||||
Set-Service -Name sshd -StartupType Automatic
|
||||
```
|
||||
|
||||
### 1.5 Windows Firewall — Ports freigeben
|
||||
|
||||
```powershell
|
||||
# PowerShell als Admin — nur interne Ports fürs LAN
|
||||
New-NetFirewallRule -DisplayName "Ollama" -Direction Inbound -LocalPort 11434 -Protocol TCP -Action Allow
|
||||
New-NetFirewallRule -DisplayName "Mana-STT" -Direction Inbound -LocalPort 3020 -Protocol TCP -Action Allow
|
||||
New-NetFirewallRule -DisplayName "Mana-TTS" -Direction Inbound -LocalPort 3022 -Protocol TCP -Action Allow
|
||||
New-NetFirewallRule -DisplayName "Mana-Image-Gen" -Direction Inbound -LocalPort 3023 -Protocol TCP -Action Allow
|
||||
New-NetFirewallRule -DisplayName "Mana-LLM" -Direction Inbound -LocalPort 3025 -Protocol TCP -Action Allow
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: NVIDIA CUDA Setup (30 Min)
|
||||
|
||||
### 2.1 CUDA Toolkit installieren
|
||||
|
||||
1. NVIDIA Treiber aktualisieren (GeForce Experience oder nvidia.com)
|
||||
2. CUDA Toolkit 12.x installieren: https://developer.nvidia.com/cuda-downloads
|
||||
3. cuDNN installieren: https://developer.nvidia.com/cudnn
|
||||
|
||||
```powershell
|
||||
# Prüfen ob CUDA funktioniert
|
||||
nvidia-smi
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Ollama mit CUDA (30 Min)
|
||||
|
||||
### 3.1 Ollama installieren
|
||||
|
||||
Download: https://ollama.com/download/windows
|
||||
|
||||
Ollama erkennt CUDA automatisch und nutzt die GPU.
|
||||
|
||||
### 3.2 Ollama als Netzwerk-Service konfigurieren
|
||||
|
||||
Standardmäßig bindet Ollama nur an `localhost`. Für LAN-Zugriff:
|
||||
|
||||
```powershell
|
||||
# Systemumgebungsvariable setzen (PowerShell als Admin)
|
||||
[System.Environment]::SetEnvironmentVariable("OLLAMA_HOST", "0.0.0.0:11434", "Machine")
|
||||
[System.Environment]::SetEnvironmentVariable("OLLAMA_ORIGINS", "*", "Machine")
|
||||
|
||||
# Ollama neu starten
|
||||
# Task Manager → Ollama beenden → Ollama App neu starten
|
||||
```
|
||||
|
||||
### 3.3 Modelle herunterladen
|
||||
|
||||
```powershell
|
||||
# Große Modelle (nutzen GPU VRAM)
|
||||
ollama pull gemma3:27b # ~16 GB — Hauptmodell
|
||||
ollama pull codestral:22b # ~14 GB — Code-Modell
|
||||
ollama pull llama3.1:70b # ~40 GB — nur wenn VRAM reicht (4-bit quant)
|
||||
|
||||
# Kompatibilitäts-Modelle (gleich wie Mac Mini)
|
||||
ollama pull gemma3:4b # ~2.5 GB
|
||||
ollama pull gemma3:12b # ~7 GB
|
||||
```
|
||||
|
||||
### 3.4 GPU-Nutzung testen
|
||||
|
||||
```powershell
|
||||
# In einem Terminal
|
||||
ollama run gemma3:27b "Sage Hallo in einem Satz"
|
||||
|
||||
# In einem zweiten Terminal: GPU-Auslastung prüfen
|
||||
nvidia-smi
|
||||
# → Ollama sollte VRAM belegen
|
||||
```
|
||||
|
||||
### 3.5 Ollama Autostart einrichten
|
||||
|
||||
Ollama für Windows startet normalerweise automatisch mit dem System (Tray-App).
|
||||
Falls nicht:
|
||||
|
||||
```powershell
|
||||
# Startup-Ordner öffnen
|
||||
shell:startup
|
||||
# Verknüpfung zu Ollama.exe dort ablegen
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Cloudflare Tunnel auf Windows-PC (30 Min)
|
||||
|
||||
### 4.1 cloudflared installieren
|
||||
|
||||
```powershell
|
||||
# Option A: winget
|
||||
winget install Cloudflare.cloudflared
|
||||
|
||||
# Option B: Download
|
||||
# https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/downloads/
|
||||
```
|
||||
|
||||
### 4.2 Tunnel erstellen
|
||||
|
||||
```powershell
|
||||
cloudflared tunnel login
|
||||
cloudflared tunnel create mana-server-gpu
|
||||
|
||||
# Tunnel-ID notieren!
|
||||
# Credentials liegen in: C:\Users\<user>\.cloudflared\<tunnel-id>.json
|
||||
```
|
||||
|
||||
### 4.3 DNS-Routen erstellen
|
||||
|
||||
```powershell
|
||||
cloudflared tunnel route dns mana-server-gpu gpu.mana.how
|
||||
cloudflared tunnel route dns mana-server-gpu llm.mana.how
|
||||
cloudflared tunnel route dns mana-server-gpu stt-v2.mana.how
|
||||
cloudflared tunnel route dns mana-server-gpu tts-v2.mana.how
|
||||
cloudflared tunnel route dns mana-server-gpu img.mana.how
|
||||
```
|
||||
|
||||
### 4.4 Tunnel-Config erstellen
|
||||
|
||||
**Datei:** `C:\Users\<user>\.cloudflared\config.yml`
|
||||
|
||||
```yaml
|
||||
tunnel: <TUNNEL_ID>
|
||||
credentials-file: C:\Users\<user>\.cloudflared\<TUNNEL_ID>.json
|
||||
|
||||
ingress:
|
||||
# SSH Access
|
||||
- hostname: gpu.mana.how
|
||||
service: ssh://localhost:22
|
||||
|
||||
# Ollama LLM direkt (für mana-llm auf Mac Mini)
|
||||
- hostname: llm.mana.how
|
||||
service: http://localhost:11434
|
||||
originRequest:
|
||||
connectTimeout: 300s
|
||||
|
||||
# STT Service (Whisper Large)
|
||||
- hostname: stt-v2.mana.how
|
||||
service: http://localhost:3020
|
||||
|
||||
# TTS Service (Kokoro + Piper)
|
||||
- hostname: tts-v2.mana.how
|
||||
service: http://localhost:3022
|
||||
|
||||
# Image Generation (FLUX.2)
|
||||
- hostname: img.mana.how
|
||||
service: http://localhost:3023
|
||||
|
||||
# Catch-all
|
||||
- service: http_status:404
|
||||
```
|
||||
|
||||
### 4.5 Tunnel als Windows-Service installieren
|
||||
|
||||
```powershell
|
||||
# PowerShell als Admin
|
||||
cloudflared service install
|
||||
# → Startet automatisch mit Windows
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: AI-Services installieren (1-2 Std)
|
||||
|
||||
### 5.1 Python-Umgebung einrichten
|
||||
|
||||
```powershell
|
||||
# Python 3.11 installieren (python.org oder winget)
|
||||
winget install Python.Python.3.11
|
||||
|
||||
# Virtuelle Umgebungen erstellen
|
||||
python -m venv C:\mana\venvs\mana-stt
|
||||
python -m venv C:\mana\venvs\mana-tts
|
||||
python -m venv C:\mana\venvs\mana-image-gen
|
||||
```
|
||||
|
||||
### 5.2 mana-stt (Speech-to-Text) — Port 3020
|
||||
|
||||
```powershell
|
||||
C:\mana\venvs\mana-stt\Scripts\activate
|
||||
|
||||
# CUDA-fähiges PyTorch installieren
|
||||
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
# Whisper installieren
|
||||
pip install faster-whisper
|
||||
pip install fastapi uvicorn python-multipart
|
||||
|
||||
# Service-Code klonen/kopieren
|
||||
git clone <repo> C:\mana\services\mana-stt
|
||||
# oder: scp vom Mac Mini
|
||||
```
|
||||
|
||||
**Windows-Service erstellen (NSSM):**
|
||||
|
||||
```powershell
|
||||
# NSSM (Non-Sucking Service Manager) installieren
|
||||
winget install NSSM
|
||||
|
||||
# Service registrieren
|
||||
nssm install mana-stt "C:\mana\venvs\mana-stt\Scripts\python.exe" "C:\mana\services\mana-stt\main.py"
|
||||
nssm set mana-stt AppDirectory "C:\mana\services\mana-stt"
|
||||
nssm set mana-stt AppEnvironmentExtra "CUDA_VISIBLE_DEVICES=0" "DEVICE=cuda" "PORT=3020"
|
||||
nssm set mana-stt Start SERVICE_AUTO_START
|
||||
nssm start mana-stt
|
||||
```
|
||||
|
||||
### 5.3 mana-tts (Text-to-Speech) — Port 3022
|
||||
|
||||
```powershell
|
||||
C:\mana\venvs\mana-tts\Scripts\activate
|
||||
|
||||
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
pip install kokoro-onnx piper-tts
|
||||
pip install fastapi uvicorn python-multipart
|
||||
|
||||
# Service registrieren
|
||||
nssm install mana-tts "C:\mana\venvs\mana-tts\Scripts\python.exe" "C:\mana\services\mana-tts\main.py"
|
||||
nssm set mana-tts AppDirectory "C:\mana\services\mana-tts"
|
||||
nssm set mana-tts AppEnvironmentExtra "DEVICE=cuda" "PORT=3022"
|
||||
nssm set mana-tts Start SERVICE_AUTO_START
|
||||
nssm start mana-tts
|
||||
```
|
||||
|
||||
### 5.4 mana-image-gen (FLUX.2) — Port 3023
|
||||
|
||||
```powershell
|
||||
C:\mana\venvs\mana-image-gen\Scripts\activate
|
||||
|
||||
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
|
||||
pip install diffusers transformers accelerate
|
||||
pip install fastapi uvicorn python-multipart
|
||||
|
||||
# FLUX.2 Modell herunterladen (~16 GB)
|
||||
# Details je nach Implementation in services/mana-image-gen/
|
||||
|
||||
nssm install mana-image-gen "C:\mana\venvs\mana-image-gen\Scripts\python.exe" "C:\mana\services\mana-image-gen\main.py"
|
||||
nssm set mana-image-gen AppDirectory "C:\mana\services\mana-image-gen"
|
||||
nssm set mana-image-gen AppEnvironmentExtra "CUDA_VISIBLE_DEVICES=0" "DEVICE=cuda" "PORT=3023"
|
||||
nssm set mana-image-gen Start SERVICE_AUTO_START
|
||||
nssm start mana-image-gen
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 6: Mac Mini umkonfigurieren (30 Min)
|
||||
|
||||
### 6.1 docker-compose.macmini.yml anpassen
|
||||
|
||||
Die AI-Service-URLs in den Docker-Containern auf dem Mac Mini müssen auf den Windows-PC zeigen.
|
||||
|
||||
**Variante A — Über LAN-IP (einfach, schnell):**
|
||||
|
||||
```yaml
|
||||
# In docker-compose.macmini.yml ändern:
|
||||
|
||||
# mana-llm Service
|
||||
mana-llm:
|
||||
environment:
|
||||
OLLAMA_URL: http://192.168.x.11:11434 # War: http://host.docker.internal:11434
|
||||
OLLAMA_DEFAULT_MODEL: gemma3:27b # War: gemma3:4b (jetzt größeres Modell möglich)
|
||||
|
||||
# chat-backend
|
||||
chat-backend:
|
||||
environment:
|
||||
OLLAMA_URL: http://192.168.x.11:11434 # War: http://host.docker.internal:11434
|
||||
|
||||
# Matrix Bots
|
||||
matrix-mana-bot:
|
||||
environment:
|
||||
OLLAMA_URL: http://192.168.x.11:11434
|
||||
STT_URL: http://192.168.x.11:3020
|
||||
TTS_URL: http://192.168.x.11:3022
|
||||
|
||||
matrix-ollama-bot:
|
||||
environment:
|
||||
OLLAMA_URL: http://192.168.x.11:11434
|
||||
|
||||
matrix-tts-bot:
|
||||
environment:
|
||||
TTS_URL: http://192.168.x.11:3022
|
||||
|
||||
matrix-stt-bot:
|
||||
environment:
|
||||
STT_URL: http://192.168.x.11:3020
|
||||
```
|
||||
|
||||
**Variante B — Über Cloudflare Tunnel (robuster, funktioniert auch remote):**
|
||||
|
||||
```yaml
|
||||
OLLAMA_URL: https://llm.mana.how
|
||||
STT_URL: https://stt-v2.mana.how
|
||||
TTS_URL: https://tts-v2.mana.how
|
||||
```
|
||||
|
||||
→ Variante A ist schneller (LAN, keine Latenz durch Cloudflare), Variante B ist flexibler.
|
||||
|
||||
**Empfehlung:** Variante A für interne Services, Cloudflare-URLs nur für externe Zugriffe.
|
||||
|
||||
### 6.2 Ollama auf Mac Mini als Fallback behalten
|
||||
|
||||
Mac Mini behält Ollama mit kleinen Modellen (`gemma3:4b`). Falls der Windows-PC offline ist, kann `mana-llm` auf den lokalen Ollama zurückfallen. Das muss im mana-llm Service konfiguriert werden (Fallback-URL).
|
||||
|
||||
### 6.3 Cloudflare Tunnel auf Mac Mini anpassen
|
||||
|
||||
Alte STT/TTS-Routen auf dem Mac Mini entfernen oder beibehalten (als Fallback):
|
||||
|
||||
```yaml
|
||||
# ~/.cloudflared/config.yml auf Mac Mini
|
||||
# Diese Routen zeigen weiterhin auf lokale Ports:
|
||||
- hostname: stt-api.mana.how # bleibt als Fallback (Mac Mini Whisper)
|
||||
service: http://localhost:3020
|
||||
|
||||
# Neue v2-Routen gehen über den Windows-PC Tunnel
|
||||
# stt-v2.mana.how → Windows-PC (konfiguriert in Phase 4)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 7: SSH-Config & Testing (30 Min)
|
||||
|
||||
### 7.1 SSH-Config auf Dev-Rechner erweitern
|
||||
|
||||
```
|
||||
# ~/.ssh/config
|
||||
Host mana-server-gpu
|
||||
HostName gpu.mana.how
|
||||
User <windows-username>
|
||||
ProxyCommand /opt/homebrew/bin/cloudflared access ssh --hostname %h
|
||||
```
|
||||
|
||||
### 7.2 Testing Checklist
|
||||
|
||||
```bash
|
||||
# Vom Dev-Rechner aus:
|
||||
|
||||
# SSH
|
||||
ssh mana-server-gpu
|
||||
|
||||
# Ollama auf Windows-PC direkt (LAN)
|
||||
curl http://192.168.x.11:11434/api/version
|
||||
|
||||
# Ollama über Cloudflare Tunnel
|
||||
curl https://llm.mana.how/api/version
|
||||
|
||||
# Großes Modell testen
|
||||
curl http://192.168.x.11:11434/api/generate \
|
||||
-d '{"model":"gemma3:27b","prompt":"Hallo!","stream":false}'
|
||||
|
||||
# STT Health
|
||||
curl http://192.168.x.11:3020/health
|
||||
|
||||
# TTS Health
|
||||
curl http://192.168.x.11:3022/health
|
||||
|
||||
# Image Gen Health
|
||||
curl http://192.168.x.11:3023/health
|
||||
|
||||
# GPU-Auslastung remote prüfen
|
||||
ssh mana-server-gpu "nvidia-smi"
|
||||
```
|
||||
|
||||
### 7.3 Von Mac Mini aus testen
|
||||
|
||||
```bash
|
||||
ssh mana-server # Auf Mac Mini verbinden
|
||||
|
||||
# Kann Mac Mini den Windows-PC erreichen?
|
||||
curl http://192.168.x.11:11434/api/version
|
||||
|
||||
# Docker-Container können Windows-PC erreichen?
|
||||
docker exec mana-service-llm curl http://192.168.x.11:11434/api/version
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Zusammenfassung: Was wo läuft
|
||||
|
||||
### Mac Mini (192.168.x.10) — bleibt wie gehabt, minus AI-Last
|
||||
|
||||
| Service | Port | Status |
|
||||
|---------|------|--------|
|
||||
| PostgreSQL | 5432 | Primary |
|
||||
| Redis | 6379 | Primary |
|
||||
| MinIO | 9000 | Unverändert |
|
||||
| mana-core-auth | 3001 | Unverändert |
|
||||
| Alle Backends | 3030-3040 | Unverändert |
|
||||
| Alle Frontends | 5000-5100 | Unverändert |
|
||||
| Matrix Stack | 4000-4090 | Unverändert |
|
||||
| Monitoring | 8000-8020 | Unverändert |
|
||||
| mana-llm (Gateway) | 3025 | Bleibt, zeigt auf Windows-PC |
|
||||
| Ollama (Fallback) | 11434 | Behält gemma3:4b |
|
||||
| mana-stt | 3020 | Kann als Fallback bleiben |
|
||||
| mana-tts | 3022 | Kann als Fallback bleiben |
|
||||
|
||||
### Windows-PC (192.168.x.11) — nur AI/ML
|
||||
|
||||
| Service | Port | GPU | Beschreibung |
|
||||
|---------|------|-----|-------------|
|
||||
| Ollama | 11434 | CUDA | gemma3:27b, codestral:22b, llama3.1:70b |
|
||||
| mana-stt | 3020 | CUDA | Whisper Large V3 |
|
||||
| mana-tts | 3022 | CUDA | Kokoro + Piper |
|
||||
| mana-image-gen | 3023 | CUDA | FLUX.2 |
|
||||
| cloudflared | — | — | Tunnel für externe Erreichbarkeit |
|
||||
|
||||
---
|
||||
|
||||
## Implementierungs-Reihenfolge
|
||||
|
||||
1. **Phase 1:** Windows-PC vorbereiten (IP, SSH, Firewall)
|
||||
2. **Phase 2:** CUDA Setup prüfen
|
||||
3. **Phase 3:** Ollama installieren + Modelle laden + testen
|
||||
4. **Phase 4:** Cloudflare Tunnel einrichten
|
||||
5. **Phase 5:** AI-Services installieren (STT, TTS, Image Gen)
|
||||
6. **Phase 6:** Mac Mini umkonfigurieren (URLs auf Windows-PC)
|
||||
7. **Phase 7:** End-to-End testen
|
||||
|
||||
**Empfehlung:** Starte mit Phase 1-3 (Ollama mit GPU). Das bringt sofort den größten Nutzen — 27B-Modelle statt 4B. Die anderen AI-Services (Phase 5) können danach einzeln migriert werden.
|
||||
Loading…
Add table
Add a link
Reference in a new issue