Table Of Contents
Table Of Contents

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.