flip.nvflare.controllers.init_training
======================================

.. py:module:: flip.nvflare.controllers.init_training


Classes
-------

.. autoapisummary::

   flip.nvflare.controllers.init_training.InitTraining


Module Contents
---------------

.. py:class:: InitTraining(model_id: str, min_clients: int = FlipConstants.MIN_CLIENTS, flip: flip.FLIP = FLIP(), cleanup_timeout: int = 600)

   Bases: :py:obj:`nvflare.apis.impl.controller.Controller`


   
   The controller that is executed pre-training and is a part of the FLIP training model

   The InitTraining workflow sends a request to the Central Hub, stating that training has initiated
   and executes the client cleanup task.

   :param model_id: ID of the model that the training is being performed under.
   :type model_id: str
   :param min_clients: Minimum number of clients. Defaults to 1 for the aggregation to take place with
                       successful results.
   :type min_clients: int, optional
   :param cleanup_timeout: Timeout for image cleanup, defaults to 600 seconds (10 minutes)
   :type cleanup_timeout: int, optional

   :raises ValueError: - when the model ID is not a valid UUID.
       - when the minimum number of clients specified is less than 1
       - when cleanup_timeout is less the 0


   .. py:attribute:: flip


   .. py:method:: start_controller(fl_ctx: nvflare.apis.fl_context.FLContext)


   .. py:method:: control_flow(abort_signal: nvflare.apis.signal.Signal, fl_ctx: nvflare.apis.fl_context.FLContext)


   .. py:method:: stop_controller(fl_ctx: nvflare.apis.fl_context.FLContext) -> None


   .. py:method:: process_result_of_unknown_task(client: nvflare.apis.client.Client, task_name, client_task_id, result: nvflare.apis.shareable.Shareable, fl_ctx: nvflare.apis.fl_context.FLContext) -> None