config_fed_client.conf

{
  # version of the configuration
  format_version = 2

  # This is the application script which will be invoked. Client can replace this script with user's own training script.
  app_script = "sgd_fl.py"

  # Additional arguments needed by the training code. For example, in lightning, these can be --trainer.batch_size=xxx.
  app_config = ""

  # Client Computing Executors.
  executors = [
    {
      # tasks the executors are defined to handle
      tasks = ["train"]

      # This particular executor
      executor {

        # Executor name : ClientAPILauncherExecutor
        # This is an executor for Client API. The underline data exchange is using Pipe.
        path = "nvflare.app_opt.pt.client_api_launcher_executor.ClientAPILauncherExecutor"

        args {
          # launcher_id is used to locate the Launcher object in "components"
          launcher_id = "launcher"

          # pipe_id is used to locate the Pipe object in "components"
          pipe_id = "pipe"

          # Timeout in seconds for waiting for a heartbeat from the training script. Defaults to 30 seconds.
          # Please refer to the class docstring for all available arguments
          heartbeat_timeout = 60

          # format of the exchange parameters
          params_exchange_format =  "raw"

          # if the transfer_type is FULL, then it will be sent directly
          # if the transfer_type is DIFF, then we will calculate the
          # difference VS received parameters and send the difference
          params_transfer_type = "FULL"

          # if train_with_evaluation is true, the executor will expect
          # the custom code need to send back both the trained parameters and the evaluation metric
          # otherwise only trained parameters are expected
          train_with_evaluation = true
        }
      }
    }
  ],

  # this defined an array of task data filters. If provided, it will control the data from server controller to client executor
  task_data_filters =  []

  # this defined an array of task result filters. If provided, it will control the result from client executor to server controller
  task_result_filters = []

  components =  [
    {
      # component id is "launcher"
      id = "launcher"

      # the class path of this component
      path = "nvflare.app_common.launchers.subprocess_launcher.SubprocessLauncher"

      args {
        # the launcher will invoke the script
        script = "python3 -u custom/{app_script}  {app_config} "
        # if launch_once is true, the SubprocessLauncher will launch once for the whole job
        # if launch_once is false, the SubprocessLauncher will launch a process for each task it receives from server
        launch_once = true
      }
    },
    {
      id = "pipe"

      path = "nvflare.fuel.utils.pipe.file_pipe.FilePipe"

      args {
        mode = "PASSIVE"
        # root_path: is the directory location of the parameters exchange.
        # You can also set it to an absolute path in your system.
        root_path = "{WORKSPACE}/{JOB_ID}/{SITE_NAME}"
      }
    }
  ]
}