fetch_ml/deployments

Docker Compose Deployments

This directory contains Docker Compose configurations for different deployment environments.

Environment Configurations

Development (docker-compose.dev.yml)

• Full development stack with monitoring
• Includes: API, Worker, Redis, MinIO (snapshots), Prometheus, Grafana, Loki, Promtail
• Optimized for local development and testing
• Usage: docker-compose -f deployments/docker-compose.dev.yml up -d

Homelab - Secure (docker-compose.homelab-secure.yml)

• Secure homelab deployment with authentication and a Caddy reverse proxy
• TLS is terminated at the reverse proxy (Approach A)
• Includes: API, Redis (password protected), Caddy reverse proxy
• Usage: docker-compose -f deployments/docker-compose.homelab-secure.yml up -d

Production (docker-compose.prod.yml)

• Production deployment configuration
• Optimized for performance and security
• External services assumed (Redis, monitoring)
• Usage: docker-compose -f deployments/docker-compose.prod.yml up -d

Note: docker-compose.prod.yml is a reproducible staging/testing harness. Real production deployments do not require Docker; you can run the Go services directly (systemd) and use Caddy for TLS/WSS termination.

TLS / WSS Policy

• The Zig CLI currently supports ws:// only (native wss:// is not implemented).
• Production deployments terminate TLS/WSS at a reverse proxy (Caddy in docker-compose.prod.yml) and keep the API server on internal ws://.
• Homelab deployments terminate TLS/WSS at a reverse proxy (Caddy) and keep the API server on internal ws://.
• Health checks in compose files should use http://localhost:9101/health when server.tls.enabled: false.

Required Volume Mounts

• base_path (experiments) must be writable by the API server.
• data_dir should be mounted if you want snapshot/dataset integrity validation via ml validate.

For the default configs:

• base_path: /data/experiments (dev/homelab configs) or /app/data/experiments (prod configs)
• data_dir: /data/active

Quick Start

# Development (most common)
docker-compose -f deployments/docker-compose.dev.yml up -d

# Check status
docker-compose -f deployments/docker-compose.dev.yml ps

# View logs
docker-compose -f deployments/docker-compose.dev.yml logs -f api-server

# Stop services
docker-compose -f deployments/docker-compose.dev.yml down

Dev: MinIO-backed snapshots (smoke test)

The dev compose file provisions a MinIO bucket and uploads a small example snapshot object at:

s3://fetchml-snapshots/snapshots/snap-1.tar.gz

To queue a task that forces the worker to pull the snapshot from MinIO:

1. Start the dev stack: docker-compose -f deployments/docker-compose.dev.yml up -d

2. Read the snapshot_sha256 printed by the init job: docker-compose -f deployments/docker-compose.dev.yml logs minio-init

3. Queue a job using the snapshot fields: ml queue <job-name> --snapshot-id snap-1 --snapshot-sha256 <snapshot_sha256>

Smoke tests

• make dev-smoke runs the development stack smoke test.

• make prod-smoke runs a Docker-based staging smoke test for the production stack, using a localhost-only Caddy configuration.

  Note: ml queue by itself will generate a random commit ID. For full provenance enforcement (manifest + dependency manifest), use ml sync ./your-project --queue so the server has real code + dependency files.

  Examples:

  • ml queue train-mnist --priority 3 --snapshot-id snap-1 --snapshot-sha256 <snapshot_sha256>
  • ml queue train-a train-b train-c --priority 5 --snapshot-id snap-1 --snapshot-sha256 <snapshot_sha256>

Environment Variables

Create a .env file in the project root:

# Grafana
GRAFANA_ADMIN_PASSWORD=your_secure_password

# API Configuration
LOG_LEVEL=info

# TLS (for secure deployments)
TLS_CERT_PATH=/app/ssl/cert.pem
TLS_KEY_PATH=/app/ssl/key.pem

Service Ports

Service	Development	Homelab	Production
API Server	9101	9101	9101
Redis	6379	6379	-
Prometheus	9090	-	-
Grafana	3000	-	-
Loki	3100	-	-
JupyterLab	8888*	8888*	-
vLLM	8000*	8000*	-

*Plugin service ports are dynamically allocated from the 8000-9000 range by the scheduler.

Plugin Services

The deployment configurations include support for interactive ML services:

Jupyter Notebook/Lab

• Image: quay.io/jupyter/base-notebook:latest
• Security: Trusted channels (conda-forge, defaults), blocked packages (http clients)
• Resources: Configurable GPU/memory limits
• Access: Via scheduler-assigned port (8000-9000 range)

vLLM Inference

• Image: vllm/vllm-openai:latest
• Features: OpenAI-compatible API, quantization support (AWQ, GPTQ, FP8)
• Model Cache: Configurable path for model storage
• Resources: Multi-GPU tensor parallelism support

Scheduler GPU Quotas

The scheduler supports GPU quota management for plugin services:

• Global Limit: Total GPUs across all plugins
• Per-User Limits: GPU and service count per user
• Per-Plugin Limits: vLLM and Jupyter-specific limits
• User Overrides: Special permissions for admins/researchers

See configs/scheduler/scheduler.yaml.example for quota configuration.

Monitoring

• Development: Full monitoring stack included
• Homelab: Basic monitoring (configurable)
• Production: External monitoring assumed

Security Notes

• If you need HTTPS externally, terminate TLS at a reverse proxy.
• API keys should be managed via environment variables
• Database credentials should use secrets management in production
• HIPAA deployments: Plugins are disabled by default for compliance