Table Of Contents
Table Of Contents

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.