Table Of Contents
Table Of Contents

DataParallelExecutorManager

class mxnet.executor_manager.DataParallelExecutorManager(symbol, ctx, train_data, arg_names, param_names, aux_names, work_load_list=None, logger=None, sym_gen=None)[source]

Helper class to manage multiple executors for data parallelism.

Parameters:
  • symbol (Symbol) – Output symbol.
  • ctx (list of Context) – Devices to run on.
  • param_names (list of str) – Name of all trainable parameters of the network.
  • arg_names (list of str) – Name of all arguments of the network.
  • aux_names (list of str) – Name of all auxiliary states of the network.
  • train_data (DataIter) – Training data iterator.
  • work_load_list (list of float or int, optional) – The list of work load for different devices, in the same order as ctx.
  • logger (logging logger) – When not specified, default logger will be used.
  • sym_gen (A function that generate new Symbols depending on different) – input shapes. Used only for bucketing.
__init__(symbol, ctx, train_data, arg_names, param_names, aux_names, work_load_list=None, logger=None, sym_gen=None)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(symbol, ctx, train_data, arg_names, …) Initialize self.
backward() Run backward on the current executor.
copy_to(arg_params, aux_params) Copy data from each executor to `arg_params and aux_params.
forward([is_train]) Run forward on the current executor.
install_monitor(monitor) Install monitor on all executors.
load_data_batch(data_batch) Load data and labels into arrays.
set_params(arg_params, aux_params) Set parameter and aux values.
update_metric(metric, labels[, pre_sliced]) Update metric with the current executor.

Attributes

aux_arrays Shared aux states.
grad_arrays Shared gradient arrays.
param_arrays Shared parameter arrays.