Django Example#

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.