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MLConnector Installation

This guide will walk you through setting up and running the MLConnector using Docker.

Prerequisites

Before you begin, ensure you have the following installed on your system:

Environment Variables

The MLConnector relies on several external components. Define the following environment variables in your shell or an .env file:

1. Docker Registry

The MLConnector dynamically creates and stores docker images for inference applications used within MYLSysOps. As such, it needs to to be able to communicate to a registry weather public, or private. This application was tested with docker registry. For further information on docker registry check.

  • DOCKER_REGISTRY_ENDPOINT: Your Docker registry endpoint
  • DOCKER_USERNAME: Your Docker registry username
  • DOCKER_PASSWORD: Your Docker registry password

2. AWS (File Storage)

The MLConnector uses an external storage service, S3 to store it's data including training data and other files. You will need to setup and S3 bucket, or S3 compatible service to complete this setup. After, please provide the following details. If you do not have access to S3 bucket, or S3 compatible service, please contact us and we can help setup a temporarly one.

  • AWS_ACCESS_URL: AWS S3 endpoint URL
  • AWS_ACCESS_KEY_ID: AWS access key ID
  • AWS_SECRET_ACCESS_KEY: AWS secret access key
  • AWS_S3_BUCKET_DATA: Name of the S3 bucket for data

3. PostgreSQL Database

This is used for internal communication of the varrious services. You can setup an external database service if you like. For simplicity you can you use the default values;

  • POSTGRES_DB: PostgreSQL database name (default, mlmodel)
  • POSTGRES_USER: PostgreSQL username (default, postgres)
  • POSTGRES_PASSWORD: PostgreSQL password (default, strongpassword)
  • PGADMIN_DEFAULT_EMAIL: pgAdmin default login email (default, user@mail.com)
  • PGADMIN_DEFAULT_PASSWORD: pgAdmin default login password (default, strongpassword)
  • DB_HOST_NAME: Database host (e.g., database, This corresponds to the name of the container)
  • DB_PORT: Database port (default: 5432)
  • DB_DRIVER: Database driver string (default, postgresql+asyncpg) NOTE: Only use an async driver

4. Northbound API Endpoint

The MLConnector communicates with part of the MYLSyops via the NORTHBOUND_API. Please set this value to the right endpoint.

  • NORTHBOUND_API_ENDPOINT: Base URL for the Northbound API (e.g., http://your-host:8000)

Running the Application

  1. Start the Docker Containers

    docker compose up -d

    This command builds and launches all required services in detached mode.

  2. View Container Logs

    docker compose logs -f

Accessing the API Documentation

Once the services are up and running, open your browser and navigate to:

http://<your-host>:8090/redoc

Replace <your-host> with your server’s hostname or localhost if running locally.

Troubleshooting

  • Port Conflicts: Ensure ports 8090 (API docs) and your database port are available.
  • Environment Variables: Verify all required variables are set. Use docker compose config to inspect the interpolated configuration.
  • Docker Connectivity: Ensure Docker Engine is running and your user has permissions to run Docker commands.
  • API Error Codes: All status codes and error messages can be accessed via: http://<your-host>:8090/redoc

License