mxnet.executor_manager.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.