nvflare.app_opt.xgboost.histogram_based_v2.mock.mock_controller module

class MockXGBController(num_rounds: int, configure_task_name='config', configure_task_timeout=60, start_task_name='start', start_task_timeout=10, job_status_check_interval: float = 2.0, max_client_op_interval: float = 600.0, progress_timeout: float = 3600.0, client_ranks=None, aggr_timeout=10.0, int_client_grpc_options=None, in_process=True)[source]

Bases: XGBController

Constructor

For the meaning of XGBoost parameters, please refer to the documentation for train API, https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.train

Parameters:

  • adaptor (adaptor_component_id - the component ID of server target)

  • rounds (num_rounds - number of)

  • horizontal/row-split (data_split_mode - 0 for)

  • vertical/column-split (1 for)

  • true (disable_version_check - If)

  • enabled (secure training is)

  • method (xgb_params - The params argument for train)

  • dictionary (xgb_options - All other arguments for train method are passed through this)

  • true

  • skipped (XGBoost version check for secure training is)

  • task (start_task_name - name of the start)

  • timeout. (start_task_timeout - time to wait for clients’ responses to the start task before)

  • task

  • timeout.

  • job (job_status_check_interval - how often to check client statuses of the)

  • client (max_client_op_interval - max amount of time allowed between XGB ops from a)

  • progress. (progress_timeout- the maximum amount of time allowed for the workflow to not make any) – In other words, at least one participating client must have made progress during this time. Otherwise, the workflow will be considered to be in trouble and the job will be aborted.

  • client_ranks – client rank assignments. If specified, must be a dict of client_name => rank. If not specified, client ranks will be randomly assigned. No matter how assigned, ranks must be consecutive integers, starting from 0.

get_adaptor(fl_ctx: FLContext)[source]