mxnet.executor_manager¶

Executor manager.

Classes

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

Bases: object

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.

Methods

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.

backward()[source]¶

Perform a backward pass on each executor.

forward(is_train=False)[source]¶

Perform a forward pass on each executor.

load_data_batch(data_batch)[source]¶

Load data and labels into arrays.

update_metric(metric, labels, pre_sliced=False)[source]¶

Update evaluation metric with label and current outputs.

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]¶

Bases: object

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.

Attributes

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

Methods

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.

property aux_arrays¶

Shared aux states.

backward()[source]¶

Run backward on the current executor.

copy_to(arg_params, aux_params)[source]¶

Copy data from each executor to `arg_params and aux_params.

Parameters

• arg_params (list of NDArray) – Target parameter arrays.

• aux_params (list of NDArray) – Target aux arrays.

Notes

• This function will inplace update the NDArrays in arg_params and aux_params.

forward(is_train=False)[source]¶

Run forward on the current executor.

property grad_arrays¶

Shared gradient arrays.

install_monitor(monitor)[source]¶

Install monitor on all executors.

load_data_batch(data_batch)[source]¶

Load data and labels into arrays.

property param_arrays¶

Shared parameter arrays.

set_params(arg_params, aux_params)[source]¶

Set parameter and aux values.

Parameters

• arg_params (list of NDArray) – Source parameter arrays

• aux_params (list of NDArray) – Source aux arrays.

update_metric(metric, labels, pre_sliced=False)[source]¶

Update metric with the current executor.