mlops-pipeline/docker-compose.yml at main · MOHD-OMER/mlops-pipeline · GitHub

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# ─────────────────────────────────────────────────────────────────────────────
# MLOps Pipeline — Docker Compose
#
# Services:
#   mlflow    — experiment tracking UI  (localhost:5000)
#   api       — FastAPI model server    (localhost:8000)
#   monitor   — one-shot drift check    (exits after run)
# ─────────────────────────────────────────────────────────────────────────────

networks:
  mlops_net:
    driver: bridge

volumes:
  mlflow_data:

services:

  # ─────────────────────────────────────────────────────────────────────────
  # MLflow tracking server
  # ─────────────────────────────────────────────────────────────────────────
  mlflow:
    image: ghcr.io/mlflow/mlflow:v2.8.1
    container_name: mlops_mlflow
    command: >
      mlflow server
        --host 0.0.0.0
        --port 5000
        --backend-store-uri sqlite:///mlruns/mlflow.db
        --default-artifact-root /mlruns/artifacts
    ports:
      - "5000:5000"
    volumes:
      - mlflow_data:/mlruns
    networks:
      - mlops_net
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:5000/health')"]
      interval: 15s
      timeout: 10s
      retries: 5
      start_period: 30s

  # ─────────────────────────────────────────────────────────────────────────
  # FastAPI model serving
  # ─────────────────────────────────────────────────────────────────────────
  api:
    build:
      context: .
      dockerfile: Dockerfile
      target: runtime
    container_name: mlops_api
    ports:
      - "8000:8000"
    environment:
      - MLFLOW_TRACKING_URI=http://mlflow:5000
      - PYTHONUNBUFFERED=1
      - PYTHONUTF8=1
    volumes:
      - ./models:/app/models:ro          # ← bind mount your existing pkl
      - ./data/processed:/app/data/processed:ro
      - ./params.yaml:/app/params.yaml:ro
      - ./reports:/app/reports
    networks:
      - mlops_net
    depends_on:
      mlflow:
        condition: service_healthy
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
  # ─────────────────────────────────────────────────────────────────────────
  # Training job (one-shot, exits after completion)
  # ─────────────────────────────────────────────────────────────────────────
  train:
    build:
      context: .
      dockerfile: Dockerfile
      target: builder
    container_name: mlops_train
    command: >
      sh -c "
        pip install -q datasets dvc &&
        python src/ingest.py &&
        python src/preprocess.py &&
        python src/train.py
      "
    environment:
      - MLFLOW_TRACKING_URI=http://mlflow:5000
      - PYTHONUNBUFFERED=1
    volumes:
      - ./data:/app/data
      - ./src:/app/src
      - ./params.yaml:/app/params.yaml
      - model_artifacts:/app/models
      - ./reports:/app/reports
    networks:
      - mlops_net
    depends_on:
      mlflow:
        condition: service_healthy
    profiles:
      - train   # only starts with: docker compose --profile train up train

  # ─────────────────────────────────────────────────────────────────────────
  # Drift monitoring (one-shot)
  # ─────────────────────────────────────────────────────────────────────────
  monitor:
    build:
      context: .
      dockerfile: Dockerfile
      target: builder
    container_name: mlops_monitor
    command: >
      sh -c "pip install -q evidently && python monitoring/monitor.py"
    volumes:
      - ./data/processed:/app/data/processed:ro
      - ./monitoring:/app/monitoring
      - ./params.yaml:/app/params.yaml
      - ./reports:/app/reports
    networks:
      - mlops_net
    profiles:
      - monitor   # docker compose --profile monitor up monitor