NVIDIA FLARE Application¶

The NVIDIA FLARE application defines how the server and client should run. Note that in the scope of one job, each site will only run one application.

The structure of the app folder needs to be:

app_folder/
    config/
        config_fed_client.json [required if this app needs to be deployed to clients]
        config_fed_server.json [required if this app needs to be deployed to server]
    custom/
        [any of your custom code].py
        [another file with custom code].py
        ...
    resources/
        log.config

Note

Note that apps can be configured to run on certain sites in a job’s deploy_map configuration. An application can also be run without a job. To do this, simply submit an app as a job and a default deploy map of all sites will be used.

Note

If the same application is going to be deployed on both server and clients, it can contain both config_fed_server.json and config_fed_client.json

Note

The configuration JSON files config_fed_server.json and config_fed_client.json may be in the root folder of the FL application or in a sub-folder (for example: config) of the FL application.

FL server configuration¶

config_fed_server.json is the FL server configuration file.

Example:

{
  "format_version": 2,
  "server": {
    "heart_beat_timeout": 600
  },
  "task_data_filters": [],
  "task_result_filters": [],
  "components": [
    {
      "id": "persistor",
      "path": "np_model_persistor.NPModelPersistor",
      "args": {}
    },
    {
      "id": "shareable_generator",
      "path": "nvflare.app_common.shareablegenerators.full_model_shareable_generator.FullModelShareableGenerator",
      "args": {}
    },
    {
      "id": "aggregator",
      "path": "nvflare.app_common.aggregators.intime_accumulate_model_aggregator.InTimeAccumulateWeightedAggregator",
      "args": {}
    }
  ],
  "workflows": [
    {
      "id": "fed_avg_ctl",
      "path": "nvflare.app_common.workflows.fed_avg.fed_avg_ctl.FedAvgController",
      "args": {
        "min_clients": 2,
        "num_rounds": 10,
        "start_round": 0,
        "wait_time_after_min_received": 10,
        "aggregator_id": "aggregator",
        "persistor_id": "persistor",
        "shareable_generator_id": "shareable_generator",
        "train_task_name": "train",
        "train_timeout": 6000
      }
    }
  ]
}

Key	Notes
format_version	The NVIDIA FLARE version for this config
server	Specify server-specific attributes like heart_beat_timeout for seconds before the heart beat times out
task_data_filters	What filters to apply to data leaving server, see Filters
task_result_filters	What filters to apply to data arriving to server, see Filters
components	All of the Components to use
workflows	What Workflows to use, see Controllers and Controller API

FL client configuration¶

config_fed_client.json is the FL client configuration file.

Example:

{
  "format_version": 2,
  "executors": [
    {
      "tasks": [
        "train"
      ],
      "executor": {
        "path": "np_trainer.NPTrainer"
      }
    }
  ],
  "task_result_filters": [],
  "task_data_filters": [],
  "components": []
}

Key	Notes
format_version	The NVIDIA FLARE version for this config
executors	The configuration for Tasks and Executors which now includes Trainers
task_data_filters	What filters to apply to data arriving at client, see Filters
task_result_filters	What filters to apply to data leaving client, Filters
components	All of the Components to use

Custom code¶

You can write your own components and bring your own code (BYOC) following the Programming Guide.

To use it in your application, put the code inside the “custom” folder of the application folder and make sure BYOC is enabled and allowed.

In your server or client config, use path to refer to that component.

Custom code config example¶

For example, with a SimpleTrainer class stored in a file my_trainer.py inside the custom folder, the client config should have the following in order to configure it as an Executor:

...
"executor": {
  "path": "my_trainer.SimpleTrainer",
  "args": {}
},
...

Note

Configuration of Executor Tasks is ignored here.

Please follow Getting Started to learn more.

Troubleshooting BYOC¶

In 2.2.1, authorization has been redesigned and BYOC is no longer controlled through settings at provisioning, but instead by each site’s authorization.json (in the local folder of the workspace). BYOC is a right and can be restricted to certain roles or even orgs or users. See Authorization: Federated Authorization for details.

Resources¶

A log.config is needed inside the resources folder. This file is for the Python logger to use. If you don’t want to customize the log behavior, you can use the same log.config from one of the example application folder.

[loggers]
keys=root,modelLogger

[handlers]
keys=consoleHandler

[formatters]
keys=fullFormatter

[logger_root]
level=INFO
handlers=consoleHandler

[logger_modelLogger]
level=DEBUG
handlers=consoleHandler
qualname=modelLogger
propagate=0

[handler_consoleHandler]
class=StreamHandler
level=DEBUG
formatter=fullFormatter
args=(sys.stdout,)

[formatter_fullFormatter]
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s