version: '3.8'

# Ollama - GPU-Accelerated ML Model Serving
# Phase 1: Foundation Setup
# Ports: 11434 (API)
# GPU: YES - Requires NVIDIA Container Toolkit
# Storage: SSD or HDD for models (models are 2-15GB each)

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    restart: unless-stopped
    ports:
      - "11434:11434"  # Ollama API endpoint
    volumes:
      # Model storage - choose based on available space:
      # SSD (faster load times): /home/jpmschweitzer/docker-data/ollama/models
      # HDD (more space): /mnt/media/ollama/models
      - /home/jpmschweitzer/docker-data/ollama/models:/root/.ollama
    environment:
      - TZ=Europe/Amsterdam
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=all
    deploy:
      resources:
        limits:
          memory: 8G
        reservations:
          memory: 1G
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    networks:
      - docker-dataplane

networks:
  docker-dataplane:
    external: true
    name: docker-dataplane

# GPU Requirements:
# - RTX 2080 Ti (11GB VRAM)
# - Suitable for 3B-13B parameter models
# - NVIDIA Container Toolkit must be installed
#
# After Deployment:
# 1. Verify GPU access: docker exec ollama nvidia-smi
# 2. Pull a model: docker exec ollama ollama pull llama3.2:3b
# 3. List models: docker exec ollama ollama list
# 4. Test inference: docker exec ollama ollama run llama3.2:3b "Hello"
# 5. Monitor GPU during inference: watch -n 1 nvidia-smi
#
# Recommended Models for RTX 2080 Ti (11GB VRAM):
# - llama3.2:3b (2GB) - Fast, general purpose
# - mistral:7b (4GB) - High quality, coding
# - codellama:7b (4GB) - Code-specialized
# - phi3:mini (2GB) - Fast reasoning
#
# API Usage:
# curl http://localhost:11434/api/generate -d '{
#   "model": "llama3.2:3b",
#   "prompt": "Why is the sky blue?",
#   "stream": false
# }'