Introduction to Docker Compose for Training Stacks

Running individual training jobs using docker run offers significant benefits for reproducibility. However, many machine learning workflows involve more than just a single training script container. You might need a database to fetch training data or log metrics, a message queue to trigger jobs, or perhaps a separate service to manage experiment configurations. Managing the lifecycle, networking, and data persistence for multiple interconnected containers using only docker run commands quickly becomes cumbersome and error-prone.

This is where Docker Compose comes into play. Docker Compose is a tool specifically designed for defining and running multi-container Docker applications. Instead of issuing complex docker run commands for each container, you describe your entire application stack, including its services, networks, and volumes, in a single declarative YAML file, typically named docker-compose.yml.

Defining Multi-Container Training Environments

Think of a scenario where your training script needs to:

1. Read hyperparameters from a configuration file served by a simple web service.
2. Fetch training data identifiers from a database.
3. Run the training process itself.
4. Write output metrics and model artifacts back to a shared volume or perhaps log them to the database.

Attempting to orchestrate this with individual docker run commands would involve manually setting up networks for communication, managing container startup order, and handling volume mounts consistently across containers. Docker Compose simplifies this significantly.

With Compose, you define each component (the training script container, the database container, the configuration service container) as a separate service within the docker-compose.yml file. Compose handles the underlying Docker operations, such as:

• Creating a shared network: Services defined in the same Compose file are automatically placed on a shared network, allowing them to easily discover and communicate with each other using their service names as hostnames.
• Managing volumes: You can define volumes within the Compose file and attach them to the relevant services, ensuring data persistence and sharing.
• Orchestrating startup and shutdown: Commands like docker-compose up start all defined services in the correct dependency order (if specified), and docker-compose down stops and removes them cleanly.

A Basic Example Structure

Consider a simplified training setup involving just the training container and a PostgreSQL database for logging results. A docker-compose.yml file might look something like this:

version: '3.8' # Specifies the Compose file version

services:
  # Service for the training script
  training:
    build: . # Instructs Compose to build an image from the Dockerfile in the current directory
    volumes:
      - ./data:/app/data # Mount local data directory into the container
      - model_output:/app/output # Mount a named volume for model output
    environment:
      - DB_HOST=db # Use the service name 'db' as the hostname
      - DB_NAME=training_logs
      - DB_USER=trainer
      - DB_PASSWORD=secretpass
    depends_on: # Ensures the database starts before the training service
      - db
    # command: python train.py --config /app/config.yaml # Optional command override

  # Service for the PostgreSQL database
  db:
    image: postgres:14-alpine # Use a pre-built PostgreSQL image
    volumes:
      - db_data:/var/lib/postgresql/data # Mount a named volume for persistent DB data
    environment:
      - POSTGRES_DB=training_logs
      - POSTGRES_USER=trainer
      - POSTGRES_PASSWORD=secretpass

# Define named volumes for persistent storage
volumes:
  db_data:
  model_output:

In this example:

• We define two services: training and db.
• The training service builds its image using a local Dockerfile.
• The db service uses a public postgres image.
• volumes are used for input data (./data bind mount), output models (model_output named volume), and persistent database storage (db_data named volume).
• environment variables configure both services, notably providing the database connection details to the training service. The DB_HOST is set to db, the service name of the PostgreSQL container, which Compose makes resolvable on the internal network.
• depends_on ensures the db service is started before the training service attempts to connect.

Diagram illustrating services defined in a docker-compose.yml file. Services like training and db communicate over a shared network, managed by Compose. Volumes (model_output, db_data) provide persistent storage. An optional configuration service is also shown.

Running the Training Stack

With the docker-compose.yml file defined, starting the entire stack is as simple as running:

docker-compose up --build

The --build flag tells Compose to build the image for the training service before starting the containers. If the image already exists and the Dockerfile or its context hasn't changed, Compose will reuse the existing image unless --build is specified.

To stop and remove the containers, network, and potentially the volumes defined in the Compose file, you use:

docker-compose down

Using Docker Compose significantly simplifies the management of multi-container setups common in more developed ML training pipelines. It provides a standardized way to define and share development or testing environments that closely mirror aspects of production deployments, enhancing reproducibility. While powerful orchestration systems like Kubernetes are often used for large-scale distributed training, Docker Compose is an invaluable tool for local development, testing, and managing moderately complex training stacks. The following sections will detail specific techniques for containerizing training scripts and handling associated aspects like configuration and GPU usage.