Common ML Stack Examples (e.g., API + DB)

Individual services, volumes, and networks are integral to Docker Compose. These elements combine to define common application stacks encountered in Machine Learning projects. A docker-compose.yml file allows for the declarative definition and management of such multi-component systems efficiently for local development and testing, mirroring more complex deployment setups.

Example 1: Inference API with a Database Backend

A frequent pattern involves deploying a trained model as an API endpoint for making predictions. Often, this API needs to interact with a database to store prediction logs, retrieve user information, or manage other application state. Docker Compose makes setting up this API-plus-database stack straightforward.

Consider an application consisting of:

API Service (api): A custom container running a web framework (like Flask or FastAPI) that loads a trained model and exposes prediction endpoints. It needs access to the database.
Database Service (db): A standard database container (like PostgreSQL or MySQL) where the API service can read/write data. Its data needs to persist across container restarts.

A diagram showing a user request hitting the API service, which communicates with the Database service over a shared Docker network. The database service uses a volume for data persistence.

Here’s a simplified docker-compose.yml representing this stack:

version: '3.8' # Specify Compose file version

services:
  api:
    build: ./api # Path to the directory containing the API's Dockerfile
    ports:
      - "5000:5000" # Map host port 5000 to container port 5000
    volumes:
      - ./api:/app # Mount local API code into the container (for development)
      - ./models:/app/models # Mount models directory
    environment:
      - DATABASE_URL=postgresql://user:password@db:5432/mydatabase
      # Other API specific config...
    depends_on:
      - db # Wait for the db service to be healthy (if healthcheck is defined)
    networks:
      - app-net

  db:
    image: postgres:14-alpine # Use an official PostgreSQL image
    volumes:
      - postgres_data:/var/lib/postgresql/data # Mount named volume for data persistence
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=mydatabase
    networks:
      - app-net
    # Optional: Add a healthcheck for depends_on
    # healthcheck:
    #   test: ["CMD-SHELL", "pg_isready -U user -d mydatabase"]
    #   interval: 10s
    #   timeout: 5s
    #   retries: 5

volumes:
  postgres_data: # Define the named volume

networks:
  app-net: # Define the custom network
    driver: bridge

Important takeaways from this example:

Services: We define two services: api and db.
Building vs. Image: The api service is built from a local Dockerfile using build: ./api, while the db service uses a pre-built image from Docker Hub (image: postgres:14-alpine).
Networking: Both services are connected to a custom network app-net. This allows the api service to connect to the database using the hostname db (the service name) and the standard PostgreSQL port 5432. The connection string postgresql://user:password@db:5432/mydatabase demonstrates this.
Volumes: A bind mount (./api:/app) is used for the api service, allowing code changes on the host to be reflected immediately in the container during development. A named volume (postgres_data) is used for the db service to ensure that the database files persist even if the db container is removed and recreated.
Environment Variables: environment is used to pass configuration. The db service uses them to initialize the database (user, password, name). The api service uses them to know how to connect to the database.
Ports: The ports mapping - "5000:5000" makes the API accessible from the host machine on port 5000.
depends_on: This ensures that the db service is started before the api service attempts to connect, although it doesn't guarantee the database inside the container is fully ready without a healthcheck.

Running docker compose up in the directory containing this file will build the API image (if needed), pull the PostgreSQL image, create the network and volume, and start both containers.

Example 2: Training Job with MLflow Tracking

Another common scenario involves running a training script within a container while logging metrics, parameters, and artifacts to an experiment tracking server like MLflow. Docker Compose can manage the training container, the MLflow tracking server, and potentially a backend database for MLflow.

Components:

Training Service (trainer): A container that runs the ML training script. It needs to know the URI of the MLflow server.
MLflow Server Service (mlflow): Runs the MLflow tracking server UI and API.
Backend Store Service (db): (Optional but recommended for robustness) A database (e.g., PostgreSQL) used by MLflow to store experiment metadata.

A diagram showing the Training service sending logs to the MLflow Server over the network. The MLflow Server stores metadata in the Backend DB and reads/writes artifacts to a shared volume/mount. A developer accesses the MLflow UI.

A possible docker-compose.yml could look like this:

version: '3.8'

services:
  trainer:
    build: ./training # Path to training script's Dockerfile context
    command: python train.py --data /data/input.csv --model-output /artifacts
    volumes:
      - ./training/src:/app # Mount training code
      - ./data:/data # Mount input data
      - mlflow_artifacts:/artifacts # Mount artifact volume
    environment:
      - MLFLOW_TRACKING_URI=http://mlflow:5001 # Tell script where MLflow server is
    depends_on:
      mlflow:
        condition: service_started # Basic dependency
      db:
        condition: service_healthy # Wait for DB (requires healthcheck)
    networks:
      - ml-net

  mlflow:
    # Consider using an official or well-maintained MLflow image,
    # or build your own if customization is needed.
    # This example assumes a pre-built image exists that takes these args.
    image: ghcr.io/mlflow/mlflow:v2.10.0
    command: >
      mlflow server
      --backend-store-uri postgresql://mlflow_user:mlflow_pass@db:5432/mlflow_db
      --default-artifact-root /mlflow_artifacts
      --host 0.0.0.0
      --port 5001
    ports:
      - "5001:5001" # Expose MLflow UI port
    volumes:
      - mlflow_artifacts:/mlflow_artifacts # Share artifact volume
    depends_on:
      db:
        condition: service_healthy # Wait for DB
    networks:
      - ml-net

  db:
    image: postgres:14-alpine
    volumes:
      - mlflow_db_data:/var/lib/postgresql/data
    environment:
      - POSTGRES_USER=mlflow_user
      - POSTGRES_PASSWORD=mlflow_pass
      - POSTGRES_DB=mlflow_db
    networks:
      - ml-net
    healthcheck: # Essential for depends_on condition: service_healthy
      test: ["CMD-SHELL", "pg_isready -U mlflow_user -d mlflow_db"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  mlflow_db_data:
  mlflow_artifacts: # Volume for storing model artifacts, parameters etc.

networks:
  ml-net:
    driver: bridge

In this setup:

The trainer service runs the training script (train.py). It receives the location of the MLflow server (http://mlflow:5001) via the MLFLOW_TRACKING_URI environment variable. The MLflow client library within the script uses this URI to connect.
The mlflow service runs the tracking server. Its command specifies the PostgreSQL database (db service) as the --backend-store-uri and a shared volume (mlflow_artifacts) as the --default-artifact-root.
The db service provides the PostgreSQL database for MLflow metadata persistence, using its own named volume (mlflow_db_data). A healthcheck is added so other services can reliably wait for it using depends_on.
A shared named volume mlflow_artifacts is mounted by both the trainer (to write artifacts) and mlflow (to read/serve artifacts).
depends_on with condition: service_healthy ensures the trainer and mlflow services wait for the db to be ready.

These examples illustrate how Docker Compose defines interconnected services typical in ML development. You can extend these patterns by adding services for data preprocessing, message queues (like Redis or RabbitMQ) for asynchronous tasks, monitoring tools, or different types of databases, all managed within a single docker-compose.yml file. This approach significantly simplifies the setup and management of your local development environment, making it closely resemble a potential production deployment structure.

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References

Overview of Docker Compose, Docker, 2024 (Docker) - Essential official documentation for declaratively defining and managing multi-container Docker applications, covering services, networks, and volumes as demonstrated in the section.
MLflow Tracking, MLflow Team, 2024 (MLflow (LF AI & Data Foundation)) - Official documentation detailing the setup and usage of the MLflow tracking server, including backend and artifact stores, directly relevant to the experiment tracking example.
Practical MLOps: How to Take a Machine Learning Model from Idea to Production, Vishnu Rachakonda, 2022 (O'Reilly Media) - A comprehensive resource on building and deploying machine learning systems in production, providing architectural patterns, best practices for MLOps, and context for containerized ML stacks.