mxnet.executor_manager.DataParallelExecutorGroup¶

class mxnet.executor_manager.DataParallelExecutorGroup(sym, arg_names, param_names, ctx, slices, train_data, shared_group=None)[source]¶

A group of executors living on different devices, for data parallelization.

Parameters

sym (Symbol) – The network configuration.
arg_names (list of str) – Equals sym.list_arguments()
param_names (list of str) – List of names of all trainable parameters.
ctx (list of Context) – List of devices for training (data parallelization).
slices (list of int) – Describes how the data parallelization splits data into different devices.
train_data (DataIter (or DataBatch)) – The dataset for training. It could be any object with provide_data and provide_label properties. Loading of actual data is not necessarily needed at this stage.
shared_grop (DataParallelExecutorGroup) – An existing executor group, if to share parameters with it.

__init__(sym, arg_names, param_names, ctx, slices, train_data, shared_group=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(sym, arg_names, param_names, ctx, …)	Initialize self.
`backward`()	Perform a backward pass on each executor.
`forward`([is_train])	Perform a forward pass on each executor.
`load_data_batch`(data_batch)	Load data and labels into arrays.
`update_metric`(metric, labels[, pre_sliced])	Update evaluation metric with label and current outputs.