NVIDIA FLARE Application

To upload and run your FL application with NVIDIA FLARE, you need to put required files into an application folder. The structure of the folder needs to be:

app_folder/
    config/
        config_fed_client.json
        config_fed_server.json
    custom/
        [any of your custom code].py
        [another file with custom code].py
        ...
    resources/
        log.config

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 - Developing Apps with NVIDIA FLARE.

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

In some examples, “name” is used to identify the class of a component instead of “path”. When a component is specified by “name”, the system will scan the nvflare package for the class name, so this notation may be simpler than using the entire path to the class for the component if it is inside the nvflare package. If the component you are using is not in the nvflare package, please use “path” and make sure that the path is accessible with the configured PYTHONPATH.

Troubleshooting BYOC

There is an enable_byoc flag for each participant that can be set at provisioning, and if that is disabled, even if you have custom code in your application folder, it will not be loaded. There is also a setting for allow_byoc through the authorization rule groups. This controls whether or not apps containing BYOC code will be allowed to be uploaded and deployed.

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