docker-compose.yml

#version: '3.8'

services:
  # =============================================================================
  # ML MODEL TRAINING SERVICE
  # =============================================================================
  # This service trains the ML model using the training data
  # It runs once to create the model, then the inference service uses it
  app-ml-train:
    build:
      context: . # Build context is the project root
      dockerfile: app-ml/Dockerfile # Use the ML app's Dockerfile
    command: ["python", "entrypoint/train.py"] # Run the training script
    volumes:
      # Mount shared folders so data persists between container restarts
      - ./data:/data # Training and production data
      - ./config:/config # Configuration files
      - ./models:/models # Trained models storage

  # =============================================================================
  # ML INFERENCE API SERVICE
  # =============================================================================
  # This service provides the REST API for making predictions
  # It loads the trained model and runs the ML pipeline in real-time
  app-ml-inference-api:
    build:
      context: . # Build context is the project root
      dockerfile: app-ml/Dockerfile # Use the ML app's Dockerfile
    command: ["python", "entrypoint/inference_api.py"] # Run the inference API
    ports:
      - "5001:5001" # Expose port 5001 for API calls
    volumes:
      # Mount shared folders to access data, config, and trained models
      - ./data:/data # Production data and predictions
      - ./config:/config # Configuration files
      - ./models:/models # Access to trained models
      # - ./common:/common           # Shared utility functions
    depends_on:
      - app-ml-train # Wait for training to complete first

  # =============================================================================
  # WEB USER INTERFACE SERVICE
  # =============================================================================
  # This service provides the web dashboard for visualizing predictions
  # It communicates with the inference API to trigger predictions
  app-ui:
    build:
      context: . # Build context is the project root
      dockerfile: app-ui/Dockerfile # Use the UI app's Dockerfile
    ports:
      - "8050:8050" # Expose port 8050 for web access
    environment:
      # Override the API host for Docker networking
      # In Docker, services can communicate using service names
      - INFERENCE_API_HOST=app-ml-inference-api
    volumes:
      # Mount shared folders to access data and config
      - ./data:/data # Access to production data and predictions
      - ./config:/config # Configuration files
      - ./models:/models # Model metadata (if needed)
      # - ./common:/common           # Shared utility functions
    depends_on:
      - app-ml-inference-api # Wait for inference API to be ready

# =============================================================================
# DOCKER COMPOSE EXPLANATION
# =============================================================================
# This file defines 3 services that work together:
#
# 1. app-ml-train: Trains the ML model once
# 2. app-ml-inference-api: Provides API for real-time predictions
# 3. app-ui: Web dashboard for visualizing results
#
# Key concepts:
# - volumes: Shared folders between host and containers
# - ports: Expose container ports to host machine
# - depends_on: Service startup order
# - environment: Override configuration for Docker networking
#
# To run: docker-compose up
# To stop: docker-compose down
# To rebuild: docker-compose up --build