learning_ai_common_plat/docs/devops/single_azure_vm

ByteLyst Single-VM Deployment

Deploy the entire ByteLyst ecosystem (30 services, 10 products) on a single raw Azure VM. Nothing pre-installed required — the script handles everything from a blank Ubuntu machine. Two files: this README and setup.sh. Copy both to the VM and run the script.

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Prerequisites

• Azure VM: Ubuntu 24.04 LTS (or 22.04), Standard_D8s_v5 (8 vCPU, 32 GB RAM) recommended
• Disk: 128 GB+ (Docker images, Cosmos emulator, Ollama models, build artifacts)
• Network: NSG allowing inbound on ports listed in the Port Map below
• GitHub access: Repos must be accessible (public or GITHUB_TOKEN for private)
• Nothing else needed — the script installs Docker, Node.js, pnpm, Gitea, Ollama, and everything

Quick Start

# 1. SSH into your Azure VM
ssh azureuser@<vm-ip>

# 2. Copy setup.sh and make executable
chmod +x setup.sh

# 3. Run — provide your GitHub username (repos are cloned via HTTPS)
#    If repos are private, also export GITHUB_TOKEN first.
sudo ./setup.sh

# 4. Wait ~15-25 minutes for full build + deploy

# 5. Verify
/opt/bytelyst/check-health.sh

Resume & Retry

Phase completion is tracked. If anything fails, you don't have to start over:

sudo ./setup.sh --phase=7          # Retry just the deploy phase
sudo ./setup.sh --resume           # Auto-resume after SSH disconnect
sudo ./setup.sh --resume-from=7    # Jump to deploy after manual fix
sudo ./setup.sh --status           # Check what's done
sudo ./setup.sh --reset            # Start completely over
sudo ./setup.sh --help             # Show full usage

What the Script Installs & Does

Software installed on the VM (from scratch)

Software	Version	Purpose
Docker CE	latest	Container runtime + Compose + BuildKit
Node.js	22 LTS	Build toolchain for TypeScript packages
pnpm	10.6.5	Package manager (workspace-aware)
Gitea	1.22 (Docker)	Local npm package registry on :3300
Ollama	latest	Local LLM inference for LocalMemGPT on :11434
git, jq, curl	latest	System utilities

Execution phases

Phase	Duration	Description
1. System	~3 min	Install Docker, Node.js 22, pnpm 10.6.5, Ollama, git, jq
2. Gitea	~1 min	Start Gitea Docker container, create admin + org + API token
3. Clone	~3 min	Clone all 11 repos to /opt/bytelyst/, strip corporate proxy from Dockerfiles
4. Build	~5 min	pnpm install && pnpm -r build all @bytelyst/* packages
5. Publish	~3 min	Publish all packages to local Gitea npm registry
6. Env	instant	Generate .env.ecosystem with Cosmos emulator key, Azurite key, JWT secret
7. Deploy	~10 min	Per-service Docker build + deploy (30 services, with fallback)
8. Verify	~1 min	Health-check all 30+ endpoints + create /opt/bytelyst/check-health.sh

Port Map (after deployment)

Infrastructure (installed by setup.sh)

Service	Port	URL
Gitea (npm registry)	3300	http://<vm-ip>:3300
Ollama (LLM API)	11434	http://<vm-ip>:11434
Cosmos Data Explorer	1234	http://<vm-ip>:1234
Azurite (Blob)	10000	—
Mailpit UI	8025	http://<vm-ip>:8025
Grafana	3000	http://<vm-ip>:3000
Traefik Dashboard	8080	http://<vm-ip>:8080

Platform Services

Service	Port	URL
platform-service	4003	http://<vm-ip>:4003/health
extraction-service	4005	http://<vm-ip>:4005/health
mcp-server	4007	http://<vm-ip>:4007/health

Platform Dashboards

Dashboard	Port	URL
Admin Console	3001	http://<vm-ip>:3001
Issue Tracker	3003	http://<vm-ip>:3003

Product Backends

Product	Port	Health
PeakPulse	4010	http://<vm-ip>:4010/health
ChronoMind	4011	http://<vm-ip>:4011/health
JarvisJr	4012	http://<vm-ip>:4012/health
NomGap	4013	http://<vm-ip>:4013/health
MindLyst	4014	http://<vm-ip>:4014/health
LysnrAI	4015	http://<vm-ip>:4015/health
NoteLett	4016	http://<vm-ip>:4016/health
FlowMonk	4017	http://<vm-ip>:4017/health
ActionTrail	4018	http://<vm-ip>:4018/health
LocalMemGPT	4019	http://<vm-ip>:4019/health

Product Web Apps

Product	Port	URL
LysnrAI Dashboard	3002	http://<vm-ip>:3002
ChronoMind	3030	http://<vm-ip>:3030
JarvisJr	3035	http://<vm-ip>:3035
FlowMonk	3040	http://<vm-ip>:3040
NoteLett	3045	http://<vm-ip>:3045
MindLyst	3050	http://<vm-ip>:3050
NomGap	3055	http://<vm-ip>:3055
ActionTrail	3060	http://<vm-ip>:3060
LocalMemGPT	3070	http://<vm-ip>:3070

Post-Deployment Commands

# Check all service health
/opt/bytelyst/check-health.sh

# View logs for a specific service
docker compose -f /opt/bytelyst/learning_ai_common_plat/docker-compose.ecosystem.yml \
  logs -f platform-service

# Restart a specific service
docker compose -f /opt/bytelyst/learning_ai_common_plat/docker-compose.ecosystem.yml \
  restart flowmonk-backend

# Stop everything
docker compose -f /opt/bytelyst/learning_ai_common_plat/docker-compose.ecosystem.yml down

# Stop and wipe all data
docker compose -f /opt/bytelyst/learning_ai_common_plat/docker-compose.ecosystem.yml down -v

Environment Variables

All optional — defaults work for most setups:

Variable	Default	Description
GITHUB_USER	saravanakumardb1	GitHub org/user to clone repos from
GITHUB_TOKEN	(empty)	Set for private repos (HTTPS auth)
GITEA_ADMIN	bytelyst-admin	Gitea admin username
GITEA_PASS	ByteLyst2026!	Gitea admin password
OLLAMA_MODEL	llama3.2:3b	Default LLM model to pull
SKIP_CLONE	0	Set 1 to skip cloning (re-runs)
SKIP_BUILD	0	Set 1 to skip package build+publish (re-runs)

CLI Flags

Flag	Description
--resume	Auto-resume from last completed phase
--resume-from=N	Resume from phase N (1-8)
--phase=N	Run ONLY phase N (useful for retrying)
--reset	Clear phase markers and start fresh
--status	Show completed phases and exit
-h, --help	Show usage help

Troubleshooting

• Cosmos emulator slow: It needs 20-30s on first boot. Services wait via health checks.
• Out of memory: Use at least 32 GB RAM. Cosmos emulator needs ~4 GB, Ollama needs ~4 GB for 3B models.
• Build failures: Check Gitea is running (docker ps | grep gitea) and packages published (curl http://localhost:3300/api/packages/bytelyst/npm/). Per-service build logs: /opt/bytelyst/.setup-state/builds/<service>.log. Retry: sudo ./setup.sh --phase=7.
• Ollama not responding: Check systemctl status ollama or curl http://localhost:11434/api/version.
• Port conflicts: Ensure nothing else runs on the listed ports before deploying.
• Corporate proxy in Dockerfiles: The script auto-strips hardcoded proxy ENVs from cloned Dockerfiles.