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Django Deployment Example

A service for aggregating model updates into a single update using Django.

Local Backend Setup (Quick setup)

  1. Setup a Pipenv with all required packages for service startup with:
make complete-install

Upon successful initialization of the service you will be prompted to create a superuser. This will require you to set a username and passsword.

  1. After initialization, the service can be spun up with the following command:
make start-service-local

When the server is started, you will be able to login at 127.0.0.1:8000/admin. For local test purposes, this should be a simple username and password for testing.

FMA Settings Description

The aggregator connector is customizable for many types of deployment the user may wish to use. The aggregator’s type is decided by the settings file (aggregator/federated_learning_project/fma_settings.py)

INSTALLED_PACKAGES = ["fma_django_connectors"]

AGGREGATOR_SETTINGS = {
    "aggregator_connector_type": "DjangoAggConnector",
    "metadata_connector": {
        "type": "DjangoMetadataConnector",
    },
    "model_data_connector": {
        "type": None
    }
    "secrets_manager": "<name of secrets manager>",
    "secrets_name": ["<name of secrets to pull>"],
}

As seen above, INSTALLED_PACKAGES references the package(s) which contain the connectors being used in the below settings. AGGREGATOR_SETTINGS is customized by setting the aggregator_connector_type. There are also the settings of the underlying connectors that the aggregator connector uses.

  • The model_data_connector is used to push and pull data to and from the resource that stores model data for your federated experiments

  • The metadata_connector is used to push and pull data to and from the resource that stores metadata for your federated experiments

Note: We talk about the model and meta data connectors in greater detail in the “Connectors” component section

The last part of the AGGREGATOR_SETTINGS is the secrets_manager and secrets_name.
These two settings indicate to the aggregator:

  • The type of secrets manager to utilize
    • The secret name(s) for the manager to query

Local Backend Setup (Granular setup)

For users looking for a more granular understanding of the example, the steps to create the environment for the service are as follows:

Installation Process

  1. Create the virtual environment
make compile-packages
make install
  1. Set environment variable for FMA_SETTINGS_MODULE, make sure the environment is setup in runserver and qcluster shells via:
export FMA_SETTINGS_MODULE=federated_learning_project.fma_settings
  1. Install redis
  • Mac OS
brew install redis
  • Linux
apt-get install redis
  1. To Create Database:
pipenv run python3 manage.py migrate
  1. To Create an admin User:
pipenv run python manage.py createsuperuser

When the server is started, you will be able to login at 127.0.0.1:8000/admin. For local test purposes, this should be a simple username and password for testing.

Starting the service

within separate consoles do the following commands:

  • Start the Django Backend:
pipenv run python manage.py runserver
  • Start the Redis Server
redis-server
  • Start the Django Q Cluster:
pipenv run python manage.py qcluster

Example Clients

Navigate to client_examples and follow the instructions that README.md to spin up python clients to interact with the service.